{
  "schema_version": 2,
  "meta": {
    "build_id": "CODEX-LINKEDIN-CONTENT-AI-SI-AGENTIC-ERA",
    "slug": "linkedin-content-ai-si-agentic-era",
    "title": "Creating LinkedIn Content for the AI, SI, and Agentic Era",
    "subtitle": "Make a native professional text post and a checked companion knowledge pack.",
    "description": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "one_sentence_description": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "target_user": "A beginner content creator or content agent reviewer: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.",
    "skill_level": "beginner",
    "estimated_time": "4–6 hours including native production; live publication optional",
    "category": "Guided Build",
    "tags": [
      "linkedin-content-ai-si-agentic-era",
      "verified practice",
      "chatgpt_codex"
    ],
    "prerequisites": [
      "Permission to use a dedicated workspace",
      "A browser and terminal",
      "Read each platform-specific prerequisite before installation"
    ],
    "required_accounts": [],
    "required_software": [
      "Modern browser",
      "Python 3.10+ for downloadable drills"
    ],
    "required_hardware": [
      "A computer with space for the practice files",
      "Second drive for the disaster-recovery checkpoint"
    ],
    "expected_cost": "Free offline practice. Optional platform production uses your own eligible account; no paid service is needed.",
    "final_outcome": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "platforms": [
      "windows",
      "macos",
      "linux"
    ],
    "version": "3.0",
    "author": "chatgpt_codex",
    "source_checked": "2026-10-06",
    "verification_status": "Created; automated browser and project checks recorded separately. Live account/OS verification is never inferred from browser completion."
  },
  "user_outcome": {
    "before": "The learner has no reproducible, checked workflow for creating linkedin content for the ai, si, and agentic era.",
    "after": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "finished_product": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources."
  },
  "learning_objectives": [
    "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
    "A professional surface map and clear audience action exist.",
    "Two observations support a professional persona and tone.",
    "A graph connects identity, role, expertise and evidence.",
    "Nine identity fields contain no invented personal achievements.",
    "Five profile surfaces align with a verified project."
  ],
  "lessons": [
    {
      "lesson_id": "L01",
      "title": "Start with the native creation experience",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.",
      "estimated_time": "10 min",
      "learning_objective": "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
      "lessons_before_this": [],
      "lessons_after_this": [
        "L02"
      ]
    },
    {
      "lesson_id": "L02",
      "title": "Map professional surfaces",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A professional surface map and clear audience action exist.",
      "lessons_before_this": [
        "L01"
      ],
      "lessons_after_this": [
        "L03"
      ]
    },
    {
      "lesson_id": "L03",
      "title": "Study two relevant career conversations",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Two observations support a professional persona and tone.",
      "lessons_before_this": [
        "L02"
      ],
      "lessons_after_this": [
        "L04"
      ]
    },
    {
      "lesson_id": "L04",
      "title": "Build the professional graph",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A graph connects identity, role, expertise and evidence.",
      "lessons_before_this": [
        "L03"
      ],
      "lessons_after_this": [
        "L05"
      ]
    },
    {
      "lesson_id": "L05",
      "title": "Write the nine identity layers",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Nine identity fields contain no invented personal achievements.",
      "lessons_before_this": [
        "L04"
      ],
      "lessons_after_this": [
        "L06"
      ]
    },
    {
      "lesson_id": "L06",
      "title": "Redesign the profile as evidence",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Five profile surfaces align with a verified project.",
      "lessons_before_this": [
        "L05"
      ],
      "lessons_after_this": [
        "L07"
      ]
    },
    {
      "lesson_id": "L07",
      "title": "Choose connected content pillars",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Pillars and a six-part series form a connected body of knowledge.",
      "lessons_before_this": [
        "L06"
      ],
      "lessons_after_this": [
        "L08"
      ]
    },
    {
      "lesson_id": "L08",
      "title": "Write the professional semantic hook",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Two hooks show a concrete professional benefit.",
      "lessons_before_this": [
        "L07"
      ],
      "lessons_after_this": [
        "L09"
      ]
    },
    {
      "lesson_id": "L09",
      "title": "Build the ten-layer search stack",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Ten consistent layers address one professional intent.",
      "lessons_before_this": [
        "L08"
      ],
      "lessons_after_this": [
        "L10"
      ]
    },
    {
      "lesson_id": "L10",
      "title": "Compare the controlled post pair",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. This is a controlled same-topic exercise; no publication result is assumed.",
      "estimated_time": "12 min",
      "learning_objective": "A/B drafts retain the same audience and interview problem.",
      "lessons_before_this": [
        "L09"
      ],
      "lessons_after_this": [
        "L11"
      ]
    },
    {
      "lesson_id": "L11",
      "title": "Write a complete knowledge post",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A complete post has a checkable evidence boundary.",
      "lessons_before_this": [
        "L10"
      ],
      "lessons_after_this": [
        "L12"
      ]
    },
    {
      "lesson_id": "L12",
      "title": "Build the case-study object",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Eight case fields identify measured and unmeasured outcomes.",
      "lessons_before_this": [
        "L11"
      ],
      "lessons_after_this": [
        "L13"
      ]
    },
    {
      "lesson_id": "L13",
      "title": "Plan original research and human signal",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A research protocol and honest originality statement exist.",
      "lessons_before_this": [
        "L12"
      ],
      "lessons_after_this": [
        "L14"
      ]
    },
    {
      "lesson_id": "L14",
      "title": "Create the ten-slide document",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Ten career-focused slide scripts have clear evidence and sources.",
      "lessons_before_this": [
        "L13"
      ],
      "lessons_after_this": [
        "L15"
      ]
    },
    {
      "lesson_id": "L15",
      "title": "Align native video and localization",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Speech, slide, caption and localized cue preserve the same claims.",
      "lessons_before_this": [
        "L14"
      ],
      "lessons_after_this": [
        "L16"
      ]
    },
    {
      "lesson_id": "L16",
      "title": "Design articles, newsletters and replies",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A short-to-long plan and a feedback loop preserve corrections.",
      "lessons_before_this": [
        "L15"
      ],
      "lessons_after_this": [
        "L17"
      ]
    },
    {
      "lesson_id": "L17",
      "title": "Test recruiting and procurement clarity",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Two evaluator personas can verify capabilities from evidence.",
      "lessons_before_this": [
        "L16"
      ],
      "lessons_after_this": [
        "L18"
      ]
    },
    {
      "lesson_id": "L18",
      "title": "Build an integrated knowledge footprint",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A four-audience review connects long-lived professional artifacts.",
      "lessons_before_this": [
        "L17"
      ],
      "lessons_after_this": [
        "L19"
      ]
    },
    {
      "lesson_id": "L19",
      "title": "Design the ten-step and six-version capstone",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Ten portfolio components and six additive versions exist.",
      "lessons_before_this": [
        "L18"
      ],
      "lessons_after_this": [
        "L20"
      ]
    },
    {
      "lesson_id": "L20",
      "title": "Plan ten experiments and a thirty-point review",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "Ten proposals and thirty preflight questions have owner and evidence.",
      "lessons_before_this": [
        "L19"
      ],
      "lessons_after_this": [
        "L21"
      ]
    },
    {
      "lesson_id": "L21",
      "title": "Recompose and approve the career package",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "learning_objective": "A professional approval record names unverified live outcomes.",
      "lessons_before_this": [
        "L20"
      ],
      "lessons_after_this": [
        "L22"
      ]
    },
    {
      "lesson_id": "L22",
      "title": "Companion knowledge and truthful claims",
      "purpose": "A dedicated folder makes mistakes easy to diagnose and keeps real project files separate.",
      "estimated_time": "10 min",
      "learning_objective": "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
      "lessons_before_this": [
        "L21"
      ],
      "lessons_after_this": [
        "L23"
      ]
    },
    {
      "lesson_id": "L23",
      "title": "Score and verify a reviewed production pack",
      "purpose": "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
      "estimated_time": "12 min",
      "learning_objective": "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
      "lessons_before_this": [
        "L22"
      ],
      "lessons_after_this": [
        "L24"
      ]
    },
    {
      "lesson_id": "L24",
      "title": "Retrieve, attribute and hand off",
      "purpose": "The relevant query returns source-linked chunks; the unrelated query abstains.",
      "estimated_time": "8 min",
      "learning_objective": "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
      "lessons_before_this": [
        "L23"
      ],
      "lessons_after_this": [
        "L25"
      ]
    },
    {
      "lesson_id": "L25",
      "title": "Build Bot Better — protect and recover",
      "purpose": "The September 23 BuildBotsBetter profile wipe removed Desktop, Documents and hidden agent memory together. Repeatable onboarding protects the knowledge needed to recover.",
      "estimated_time": "30 min",
      "learning_objective": "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
      "lessons_before_this": [
        "L24"
      ],
      "lessons_after_this": []
    }
  ],
  "steps": [
    {
      "title": "Open the native draft studio",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.",
      "user_action": "Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two professional text post drafts for Interview project evidence. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.",
      "estimated_time": "10 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "studio.html",
        "strategy.json",
        "AGENT_OPERATING_MANUAL.md",
        "agent-training.json"
      ],
      "expected_result": "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
      "resources": [
        "studio.html",
        "strategy.json",
        "AGENT_OPERATING_MANUAL.md",
        "agent-training.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Open the native draft studio",
        "success_condition": "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
        "how_to_verify": "Record the studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify open the native draft studio?",
          "answer": "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use studio.html, strategy.json, AGENT_OPERATING_MANUAL.md, agent-training.json. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to open the native draft studio. The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
        "demo": "Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two professional text post drafts for Interview project evidence. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate open the native draft studio",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two professional text post drafts for Interview project evidence. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture. The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
        "on_screen_text": [
          "Open the native draft studio",
          "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries."
        ],
        "screen_recording_instructions": [
          "Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two professional text post drafts for Interview project evidence. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture.",
          "Inspect the result: The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Download the resource ZIP and extract it into a new private folder. Open studio.html directly in your browser. Compare the two professional text post drafts for Interview project evidence. Read AGENT_OPERATING_MANUAL.md and all twenty points in agent-training.json: native CTA/AI, concrete inputs, draft outputs and stop rules. Inspect strategy.json and assign an artifact to each maturity stage. No account or terminal is needed for the studio; do not publish a synthetic fixture.",
          "The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries."
        ]
      },
      "step_id": "L01-S01",
      "lesson_id": "L01"
    },
    {
      "title": "Map professional surfaces",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Inventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A professional surface map and clear audience action exist.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Map professional surfaces",
        "success_condition": "A professional surface map and clear audience action exist.",
        "how_to_verify": "Record a professional surface map and clear audience action exist. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A professional surface map and clear audience action exist.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A professional surface map and clear audience action exist.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for map professional surfaces?",
          "answer": "A professional surface map and clear audience action exist. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to map professional surfaces. A professional surface map and clear audience action exist.",
        "demo": "Inventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate map professional surfaces",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Inventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action. A professional surface map and clear audience action exist.",
        "on_screen_text": [
          "Map professional surfaces",
          "A professional surface map and clear audience action exist."
        ],
        "screen_recording_instructions": [
          "Inventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.",
          "Inspect the result: A professional surface map and clear audience action exist.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A professional surface map and clear audience action exist."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Inventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.",
          "A professional surface map and clear audience action exist."
        ]
      },
      "step_id": "L02-S01",
      "lesson_id": "L02"
    },
    {
      "title": "Study two relevant career conversations",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Read two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Two observations support a professional persona and tone.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Study two relevant career conversations",
        "success_condition": "Two observations support a professional persona and tone.",
        "how_to_verify": "Record two observations support a professional persona and tone. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Two observations support a professional persona and tone.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Two observations support a professional persona and tone.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for study two relevant career conversations?",
          "answer": "Two observations support a professional persona and tone. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to study two relevant career conversations. Two observations support a professional persona and tone.",
        "demo": "Read two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate study two relevant career conversations",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Read two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system. Two observations support a professional persona and tone.",
        "on_screen_text": [
          "Study two relevant career conversations",
          "Two observations support a professional persona and tone."
        ],
        "screen_recording_instructions": [
          "Read two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.",
          "Inspect the result: Two observations support a professional persona and tone.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Two observations support a professional persona and tone."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Read two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.",
          "Two observations support a professional persona and tone."
        ]
      },
      "step_id": "L03-S01",
      "lesson_id": "L03"
    },
    {
      "title": "Build the professional graph",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A graph connects identity, role, expertise and evidence.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Build the professional graph",
        "success_condition": "A graph connects identity, role, expertise and evidence.",
        "how_to_verify": "Record a graph connects identity, role, expertise and evidence. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A graph connects identity, role, expertise and evidence.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A graph connects identity, role, expertise and evidence.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for build the professional graph?",
          "answer": "A graph connects identity, role, expertise and evidence. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to build the professional graph. A graph connects identity, role, expertise and evidence.",
        "demo": "Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate build the professional graph",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim. A graph connects identity, role, expertise and evidence.",
        "on_screen_text": [
          "Build the professional graph",
          "A graph connects identity, role, expertise and evidence."
        ],
        "screen_recording_instructions": [
          "Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.",
          "Inspect the result: A graph connects identity, role, expertise and evidence.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A graph connects identity, role, expertise and evidence."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.",
          "A graph connects identity, role, expertise and evidence."
        ]
      },
      "step_id": "L04-S01",
      "lesson_id": "L04"
    },
    {
      "title": "Write the nine identity layers",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Describe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Nine identity fields contain no invented personal achievements.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Write the nine identity layers",
        "success_condition": "Nine identity fields contain no invented personal achievements.",
        "how_to_verify": "Record nine identity fields contain no invented personal achievements. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Nine identity fields contain no invented personal achievements.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Nine identity fields contain no invented personal achievements.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for write the nine identity layers?",
          "answer": "Nine identity fields contain no invented personal achievements. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to write the nine identity layers. Nine identity fields contain no invented personal achievements.",
        "demo": "Describe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate write the nine identity layers",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Describe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples. Nine identity fields contain no invented personal achievements.",
        "on_screen_text": [
          "Write the nine identity layers",
          "Nine identity fields contain no invented personal achievements."
        ],
        "screen_recording_instructions": [
          "Describe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.",
          "Inspect the result: Nine identity fields contain no invented personal achievements.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Nine identity fields contain no invented personal achievements."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Describe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.",
          "Nine identity fields contain no invented personal achievements."
        ]
      },
      "step_id": "L05-S01",
      "lesson_id": "L05"
    },
    {
      "title": "Redesign the profile as evidence",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Draft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Five profile surfaces align with a verified project.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Redesign the profile as evidence",
        "success_condition": "Five profile surfaces align with a verified project.",
        "how_to_verify": "Record five profile surfaces align with a verified project. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Five profile surfaces align with a verified project.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Five profile surfaces align with a verified project.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for redesign the profile as evidence?",
          "answer": "Five profile surfaces align with a verified project. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to redesign the profile as evidence. Five profile surfaces align with a verified project.",
        "demo": "Draft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate redesign the profile as evidence",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Draft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing. Five profile surfaces align with a verified project.",
        "on_screen_text": [
          "Redesign the profile as evidence",
          "Five profile surfaces align with a verified project."
        ],
        "screen_recording_instructions": [
          "Draft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.",
          "Inspect the result: Five profile surfaces align with a verified project.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Five profile surfaces align with a verified project."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Draft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.",
          "Five profile surfaces align with a verified project."
        ]
      },
      "step_id": "L06-S01",
      "lesson_id": "L06"
    },
    {
      "title": "Choose connected content pillars",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Select three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Pillars and a six-part series form a connected body of knowledge.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Choose connected content pillars",
        "success_condition": "Pillars and a six-part series form a connected body of knowledge.",
        "how_to_verify": "Record pillars and a six-part series form a connected body of knowledge. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Pillars and a six-part series form a connected body of knowledge.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Pillars and a six-part series form a connected body of knowledge.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for choose connected content pillars?",
          "answer": "Pillars and a six-part series form a connected body of knowledge. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to choose connected content pillars. Pillars and a six-part series form a connected body of knowledge.",
        "demo": "Select three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate choose connected content pillars",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Select three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem. Pillars and a six-part series form a connected body of knowledge.",
        "on_screen_text": [
          "Choose connected content pillars",
          "Pillars and a six-part series form a connected body of knowledge."
        ],
        "screen_recording_instructions": [
          "Select three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.",
          "Inspect the result: Pillars and a six-part series form a connected body of knowledge.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Pillars and a six-part series form a connected body of knowledge."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Select three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.",
          "Pillars and a six-part series form a connected body of knowledge."
        ]
      },
      "step_id": "L07-S01",
      "lesson_id": "L07"
    },
    {
      "title": "Write the professional semantic hook",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Two hooks show a concrete professional benefit.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Write the professional semantic hook",
        "success_condition": "Two hooks show a concrete professional benefit.",
        "how_to_verify": "Record two hooks show a concrete professional benefit. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Two hooks show a concrete professional benefit.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Two hooks show a concrete professional benefit.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for write the professional semantic hook?",
          "answer": "Two hooks show a concrete professional benefit. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to write the professional semantic hook. Two hooks show a concrete professional benefit.",
        "demo": "Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate write the professional semantic hook",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results. Two hooks show a concrete professional benefit.",
        "on_screen_text": [
          "Write the professional semantic hook",
          "Two hooks show a concrete professional benefit."
        ],
        "screen_recording_instructions": [
          "Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.",
          "Inspect the result: Two hooks show a concrete professional benefit.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Two hooks show a concrete professional benefit."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.",
          "Two hooks show a concrete professional benefit."
        ]
      },
      "step_id": "L08-S01",
      "lesson_id": "L08"
    },
    {
      "title": "Build the ten-layer search stack",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Map speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Ten consistent layers address one professional intent.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Build the ten-layer search stack",
        "success_condition": "Ten consistent layers address one professional intent.",
        "how_to_verify": "Record ten consistent layers address one professional intent. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Ten consistent layers address one professional intent.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Ten consistent layers address one professional intent.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for build the ten-layer search stack?",
          "answer": "Ten consistent layers address one professional intent. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to build the ten-layer search stack. Ten consistent layers address one professional intent.",
        "demo": "Map speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate build the ten-layer search stack",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Map speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces. Ten consistent layers address one professional intent.",
        "on_screen_text": [
          "Build the ten-layer search stack",
          "Ten consistent layers address one professional intent."
        ],
        "screen_recording_instructions": [
          "Map speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces.",
          "Inspect the result: Ten consistent layers address one professional intent.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Ten consistent layers address one professional intent."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Map speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces.",
          "Ten consistent layers address one professional intent."
        ]
      },
      "step_id": "L09-S01",
      "lesson_id": "L09"
    },
    {
      "title": "Compare the controlled post pair",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. This is a controlled same-topic exercise; no publication result is assumed.",
      "user_action": "Use studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. This is a controlled same-topic exercise; no publication result is assumed.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "studio.html",
        "versions.json"
      ],
      "expected_result": "A/B drafts retain the same audience and interview problem.",
      "resources": [
        "studio.html",
        "versions.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Compare the controlled post pair",
        "success_condition": "A/B drafts retain the same audience and interview problem.",
        "how_to_verify": "Record a/B drafts retain the same audience and interview problem. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A/B drafts retain the same audience and interview problem.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A/B drafts retain the same audience and interview problem.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for compare the controlled post pair?",
          "answer": "A/B drafts retain the same audience and interview problem. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to compare the controlled post pair. A/B drafts retain the same audience and interview problem.",
        "demo": "Use studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate compare the controlled post pair",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Use studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question. A/B drafts retain the same audience and interview problem.",
        "on_screen_text": [
          "Compare the controlled post pair",
          "A/B drafts retain the same audience and interview problem."
        ],
        "screen_recording_instructions": [
          "Use studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.",
          "Inspect the result: A/B drafts retain the same audience and interview problem.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A/B drafts retain the same audience and interview problem."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Use studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.",
          "A/B drafts retain the same audience and interview problem."
        ]
      },
      "step_id": "L10-S01",
      "lesson_id": "L10"
    },
    {
      "title": "Write a complete knowledge post",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A complete post has a checkable evidence boundary.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Write a complete knowledge post",
        "success_condition": "A complete post has a checkable evidence boundary.",
        "how_to_verify": "Record a complete post has a checkable evidence boundary. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A complete post has a checkable evidence boundary.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A complete post has a checkable evidence boundary.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for write a complete knowledge post?",
          "answer": "A complete post has a checkable evidence boundary. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to write a complete knowledge post. A complete post has a checkable evidence boundary.",
        "demo": "Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate write a complete knowledge post",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial. A complete post has a checkable evidence boundary.",
        "on_screen_text": [
          "Write a complete knowledge post",
          "A complete post has a checkable evidence boundary."
        ],
        "screen_recording_instructions": [
          "Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.",
          "Inspect the result: A complete post has a checkable evidence boundary.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A complete post has a checkable evidence boundary."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.",
          "A complete post has a checkable evidence boundary."
        ]
      },
      "step_id": "L11-S01",
      "lesson_id": "L11"
    },
    {
      "title": "Build the case-study object",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Document who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Eight case fields identify measured and unmeasured outcomes.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Build the case-study object",
        "success_condition": "Eight case fields identify measured and unmeasured outcomes.",
        "how_to_verify": "Record eight case fields identify measured and unmeasured outcomes. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Eight case fields identify measured and unmeasured outcomes.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Eight case fields identify measured and unmeasured outcomes.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for build the case-study object?",
          "answer": "Eight case fields identify measured and unmeasured outcomes. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to build the case-study object. Eight case fields identify measured and unmeasured outcomes.",
        "demo": "Document who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate build the case-study object",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Document who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material. Eight case fields identify measured and unmeasured outcomes.",
        "on_screen_text": [
          "Build the case-study object",
          "Eight case fields identify measured and unmeasured outcomes."
        ],
        "screen_recording_instructions": [
          "Document who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material.",
          "Inspect the result: Eight case fields identify measured and unmeasured outcomes.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Eight case fields identify measured and unmeasured outcomes."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Document who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material.",
          "Eight case fields identify measured and unmeasured outcomes."
        ]
      },
      "step_id": "L12-S01",
      "lesson_id": "L12"
    },
    {
      "title": "Plan original research and human signal",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A research protocol and honest originality statement exist.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Plan original research and human signal",
        "success_condition": "A research protocol and honest originality statement exist.",
        "how_to_verify": "Record a research protocol and honest originality statement exist. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A research protocol and honest originality statement exist.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A research protocol and honest originality statement exist.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for plan original research and human signal?",
          "answer": "A research protocol and honest originality statement exist. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to plan original research and human signal. A research protocol and honest originality statement exist.",
        "demo": "Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate plan original research and human signal",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements. A research protocol and honest originality statement exist.",
        "on_screen_text": [
          "Plan original research and human signal",
          "A research protocol and honest originality statement exist."
        ],
        "screen_recording_instructions": [
          "Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.",
          "Inspect the result: A research protocol and honest originality statement exist.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A research protocol and honest originality statement exist."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.",
          "A research protocol and honest originality statement exist."
        ]
      },
      "step_id": "L13-S01",
      "lesson_id": "L13"
    },
    {
      "title": "Create the ten-slide document",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Script problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Ten career-focused slide scripts have clear evidence and sources.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Create the ten-slide document",
        "success_condition": "Ten career-focused slide scripts have clear evidence and sources.",
        "how_to_verify": "Record ten career-focused slide scripts have clear evidence and sources. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Ten career-focused slide scripts have clear evidence and sources.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Ten career-focused slide scripts have clear evidence and sources.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for create the ten-slide document?",
          "answer": "Ten career-focused slide scripts have clear evidence and sources. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to create the ten-slide document. Ten career-focused slide scripts have clear evidence and sources.",
        "demo": "Script problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate create the ten-slide document",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Script problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text. Ten career-focused slide scripts have clear evidence and sources.",
        "on_screen_text": [
          "Create the ten-slide document",
          "Ten career-focused slide scripts have clear evidence and sources."
        ],
        "screen_recording_instructions": [
          "Script problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.",
          "Inspect the result: Ten career-focused slide scripts have clear evidence and sources.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Ten career-focused slide scripts have clear evidence and sources."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Script problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.",
          "Ten career-focused slide scripts have clear evidence and sources."
        ]
      },
      "step_id": "L14-S01",
      "lesson_id": "L14"
    },
    {
      "title": "Align native video and localization",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Storyboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Speech, slide, caption and localized cue preserve the same claims.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Align native video and localization",
        "success_condition": "Speech, slide, caption and localized cue preserve the same claims.",
        "how_to_verify": "Record speech, slide, caption and localized cue preserve the same claims. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Speech, slide, caption and localized cue preserve the same claims.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Speech, slide, caption and localized cue preserve the same claims.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for align native video and localization?",
          "answer": "Speech, slide, caption and localized cue preserve the same claims. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to align native video and localization. Speech, slide, caption and localized cue preserve the same claims.",
        "demo": "Storyboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate align native video and localization",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Storyboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable. Speech, slide, caption and localized cue preserve the same claims.",
        "on_screen_text": [
          "Align native video and localization",
          "Speech, slide, caption and localized cue preserve the same claims."
        ],
        "screen_recording_instructions": [
          "Storyboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.",
          "Inspect the result: Speech, slide, caption and localized cue preserve the same claims.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Speech, slide, caption and localized cue preserve the same claims."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Storyboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.",
          "Speech, slide, caption and localized cue preserve the same claims."
        ]
      },
      "step_id": "L15-S01",
      "lesson_id": "L15"
    },
    {
      "title": "Design articles, newsletters and replies",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Outline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A short-to-long plan and a feedback loop preserve corrections.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Design articles, newsletters and replies",
        "success_condition": "A short-to-long plan and a feedback loop preserve corrections.",
        "how_to_verify": "Record a short-to-long plan and a feedback loop preserve corrections. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A short-to-long plan and a feedback loop preserve corrections.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A short-to-long plan and a feedback loop preserve corrections.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for design articles, newsletters and replies?",
          "answer": "A short-to-long plan and a feedback loop preserve corrections. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to design articles, newsletters and replies. A short-to-long plan and a feedback loop preserve corrections.",
        "demo": "Outline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate design articles, newsletters and replies",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Outline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review. A short-to-long plan and a feedback loop preserve corrections.",
        "on_screen_text": [
          "Design articles, newsletters and replies",
          "A short-to-long plan and a feedback loop preserve corrections."
        ],
        "screen_recording_instructions": [
          "Outline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review.",
          "Inspect the result: A short-to-long plan and a feedback loop preserve corrections.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A short-to-long plan and a feedback loop preserve corrections."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Outline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review.",
          "A short-to-long plan and a feedback loop preserve corrections."
        ]
      },
      "step_id": "L16-S01",
      "lesson_id": "L16"
    },
    {
      "title": "Test recruiting and procurement clarity",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Write a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "Two evaluator personas can verify capabilities from evidence.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Test recruiting and procurement clarity",
        "success_condition": "Two evaluator personas can verify capabilities from evidence.",
        "how_to_verify": "Record two evaluator personas can verify capabilities from evidence. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Two evaluator personas can verify capabilities from evidence.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Two evaluator personas can verify capabilities from evidence.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for test recruiting and procurement clarity?",
          "answer": "Two evaluator personas can verify capabilities from evidence. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to test recruiting and procurement clarity. Two evaluator personas can verify capabilities from evidence.",
        "demo": "Write a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate test recruiting and procurement clarity",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Write a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach. Two evaluator personas can verify capabilities from evidence.",
        "on_screen_text": [
          "Test recruiting and procurement clarity",
          "Two evaluator personas can verify capabilities from evidence."
        ],
        "screen_recording_instructions": [
          "Write a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.",
          "Inspect the result: Two evaluator personas can verify capabilities from evidence.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Two evaluator personas can verify capabilities from evidence."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Write a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.",
          "Two evaluator personas can verify capabilities from evidence."
        ]
      },
      "step_id": "L17-S01",
      "lesson_id": "L17"
    },
    {
      "title": "Build an integrated knowledge footprint",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Connect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json"
      ],
      "expected_result": "A four-audience review connects long-lived professional artifacts.",
      "resources": [
        "native-production.md",
        "native.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Build an integrated knowledge footprint",
        "success_condition": "A four-audience review connects long-lived professional artifacts.",
        "how_to_verify": "Record a four-audience review connects long-lived professional artifacts. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A four-audience review connects long-lived professional artifacts.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A four-audience review connects long-lived professional artifacts.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for build an integrated knowledge footprint?",
          "answer": "A four-audience review connects long-lived professional artifacts. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to build an integrated knowledge footprint. A four-audience review connects long-lived professional artifacts.",
        "demo": "Connect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate build an integrated knowledge footprint",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Connect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach. A four-audience review connects long-lived professional artifacts.",
        "on_screen_text": [
          "Build an integrated knowledge footprint",
          "A four-audience review connects long-lived professional artifacts."
        ],
        "screen_recording_instructions": [
          "Connect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.",
          "Inspect the result: A four-audience review connects long-lived professional artifacts.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A four-audience review connects long-lived professional artifacts."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Connect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.",
          "A four-audience review connects long-lived professional artifacts."
        ]
      },
      "step_id": "L18-S01",
      "lesson_id": "L18"
    },
    {
      "title": "Design the ten-step and six-version capstone",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Complete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "capstone.md"
      ],
      "expected_result": "Ten portfolio components and six additive versions exist.",
      "resources": [
        "capstone.md"
      ],
      "checkpoint": {
        "what_is_being_checked": "Design the ten-step and six-version capstone",
        "success_condition": "Ten portfolio components and six additive versions exist.",
        "how_to_verify": "Record ten portfolio components and six additive versions exist. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Ten portfolio components and six additive versions exist.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Ten portfolio components and six additive versions exist.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for design the ten-step and six-version capstone?",
          "answer": "Ten portfolio components and six additive versions exist. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to design the ten-step and six-version capstone. Ten portfolio components and six additive versions exist.",
        "demo": "Complete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate design the ten-step and six-version capstone",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Complete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post. Ten portfolio components and six additive versions exist.",
        "on_screen_text": [
          "Design the ten-step and six-version capstone",
          "Ten portfolio components and six additive versions exist."
        ],
        "screen_recording_instructions": [
          "Complete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.",
          "Inspect the result: Ten portfolio components and six additive versions exist.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Ten portfolio components and six additive versions exist."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Complete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.",
          "Ten portfolio components and six additive versions exist."
        ]
      },
      "step_id": "L19-S01",
      "lesson_id": "L19"
    },
    {
      "title": "Plan ten experiments and a thirty-point review",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Define entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "experiments.md",
        "metrics.json"
      ],
      "expected_result": "Ten proposals and thirty preflight questions have owner and evidence.",
      "resources": [
        "experiments.md",
        "metrics.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Plan ten experiments and a thirty-point review",
        "success_condition": "Ten proposals and thirty preflight questions have owner and evidence.",
        "how_to_verify": "Record ten proposals and thirty preflight questions have owner and evidence. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Ten proposals and thirty preflight questions have owner and evidence.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Ten proposals and thirty preflight questions have owner and evidence.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for plan ten experiments and a thirty-point review?",
          "answer": "Ten proposals and thirty preflight questions have owner and evidence. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to plan ten experiments and a thirty-point review. Ten proposals and thirty preflight questions have owner and evidence.",
        "demo": "Define entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate plan ten experiments and a thirty-point review",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Define entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions. Ten proposals and thirty preflight questions have owner and evidence.",
        "on_screen_text": [
          "Plan ten experiments and a thirty-point review",
          "Ten proposals and thirty preflight questions have owner and evidence."
        ],
        "screen_recording_instructions": [
          "Define entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.",
          "Inspect the result: Ten proposals and thirty preflight questions have owner and evidence.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Ten proposals and thirty preflight questions have owner and evidence."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Define entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.",
          "Ten proposals and thirty preflight questions have owner and evidence."
        ]
      },
      "step_id": "L20-S01",
      "lesson_id": "L20"
    },
    {
      "title": "Recompose and approve the career package",
      "purpose": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "user_action": "Create LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA.",
      "explanation": "A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures. Keep the saved artifact and a specific observation together.",
      "estimated_time": "12 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "native-production.md",
        "native.json",
        "AGENT_OPERATING_MANUAL.md",
        "agent-training.json"
      ],
      "expected_result": "A professional approval record names unverified live outcomes.",
      "resources": [
        "native-production.md",
        "native.json",
        "AGENT_OPERATING_MANUAL.md",
        "agent-training.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Recompose and approve the career package",
        "success_condition": "A professional approval record names unverified live outcomes.",
        "how_to_verify": "Record a professional approval record names unverified live outcomes. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A professional approval record names unverified live outcomes.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A professional approval record names unverified live outcomes.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "What is a useful result for recompose and approve the career package?",
          "answer": "A professional approval record names unverified live outcomes. Include the actual draft, source date or human observation rather than a claim that the algorithm will reward it."
        },
        {
          "question": "Can I use a different topic?",
          "answer": "Yes. Keep the same audience, topic, format and promise across your comparison versions. Update every entity/source/knowledge artifact consistently."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to recompose and approve the career package. A professional approval record names unverified live outcomes.",
        "demo": "Create LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate recompose and approve the career package",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Create LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA. A professional approval record names unverified live outcomes.",
        "on_screen_text": [
          "Recompose and approve the career package",
          "A professional approval record names unverified live outcomes."
        ],
        "screen_recording_instructions": [
          "Create LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA.",
          "Inspect the result: A professional approval record names unverified live outcomes.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A professional approval record names unverified live outcomes."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Create LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI. Review the primary native AI source in AGENT_OPERATING_MANUAL.md. Record documented inputs/outputs, actual account availability (or NOT VERIFIED), a useful native application and a privacy/failure limit. Use this platform’s CTA chain from agent-training.json, rather than importing another platform’s CTA.",
          "A professional approval record names unverified live outcomes."
        ]
      },
      "step_id": "L21-S01",
      "lesson_id": "L21"
    },
    {
      "title": "Set up the practice folder",
      "purpose": "A dedicated folder makes mistakes easy to diagnose and keeps real project files separate.",
      "user_action": "Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder.",
      "explanation": "A dedicated folder makes mistakes easy to diagnose and keeps real project files separate.",
      "estimated_time": "6 min",
      "action_type": "command",
      "commands": {
        "windows": "python --version\nGet-Location\nGet-ChildItem",
        "macos": "python3 --version\npwd\nls",
        "linux": "python3 --version\npwd\nls"
      },
      "command_type": "shell",
      "required_inputs": [],
      "expected_result": "Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
      "resources": [],
      "checkpoint": {
        "what_is_being_checked": "Set up the practice folder",
        "success_condition": "Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
        "how_to_verify": "Record python reports version 3.10 or newer and the terminal is in the extracted resource folder. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "Python is 3.10 or newer.",
          "The listed files match the downloaded resources."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "Python or a file is not found",
          "likely_cause": "The terminal may be outside the extracted ZIP or Python may not be on PATH.",
          "diagnostic_question": "Does the file list include the resource named by the next step?",
          "fix": "Install Python from python.org with the PATH option; reopen the terminal and change into the extracted folder.",
          "alternative_fix": "Use py -3 on Windows if the Python launcher is installed. macOS/Linux commands use python3.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Install Python from python.org with the PATH option; reopen the terminal and change into the extracted folder."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify set up the practice folder?",
          "answer": "Python reports version 3.10 or newer and the terminal is in the extracted resource folder. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use your saved work from the previous step. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to set up the practice folder. Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
        "demo": "Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Install Python from python.org with the PATH option; reopen the terminal and change into the extracted folder.",
        "help": "Does the file list include the resource named by the next step?"
      },
      "video": {
        "purpose": "Demonstrate set up the practice folder",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder. Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
        "on_screen_text": [
          "Set up the practice folder",
          "Python reports version 3.10 or newer and the terminal is in the extracted resource folder."
        ],
        "screen_recording_instructions": [
          "Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder.",
          "Inspect the result: Python reports version 3.10 or newer and the terminal is in the extracted resource folder.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "Python reports version 3.10 or newer and the terminal is in the extracted resource folder."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Download the resource ZIP at the top of this Build. Extract it into a new folder you own (for example C:\\BBBPractice on Windows, or ~/bbb-practice on macOS/Linux). Open a terminal in the extracted folder. Run the version command for your platform. All subsequent commands assume this folder.",
          "Python reports version 3.10 or newer and the terminal is in the extracted resource folder."
        ]
      },
      "step_id": "L22-S01",
      "lesson_id": "L22"
    },
    {
      "title": "Audit the mechanism and claim ledger",
      "purpose": "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
      "user_action": "Read mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "mechanisms.md",
        "claims.json",
        "knowledge.json",
        "rag.jsonl"
      ],
      "expected_result": "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
      "resources": [
        "mechanisms.md",
        "claims.json",
        "knowledge.json",
        "rag.jsonl"
      ],
      "checkpoint": {
        "what_is_being_checked": "Audit the mechanism and claim ledger",
        "success_condition": "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
        "how_to_verify": "Record the ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify audit the mechanism and claim ledger?",
          "answer": "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use mechanisms.md, claims.json, knowledge.json, rag.jsonl. Work in the same practice folder."
        }
      ],
      "quiz": {
        "question": "Does a readable public post guarantee model training?",
        "answer_type": "single_choice",
        "choices": [
          "No; access, selection and training decisions are separate from post clarity.",
          "Yes; entity names force weight updates."
        ],
        "correct_answer": 0,
        "explanation": "Retrieval supplies inference context; training changes weights."
      },
      "coach": {
        "intro": "We are going to audit the mechanism and claim ledger. The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
        "demo": "Read mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate audit the mechanism and claim ledger",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Read mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source. The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
        "on_screen_text": [
          "Audit the mechanism and claim ledger",
          "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone."
        ],
        "screen_recording_instructions": [
          "Read mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source.",
          "Inspect the result: The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Read mechanisms.md and claims.json. Attach a direct source and date to established claims; distinguish hypothesis from evidence and future SI from deployed features. Edit knowledge.json and rag.jsonl so each answer stands alone with named entities and a source.",
          "The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone."
        ]
      },
      "step_id": "L22-S02",
      "lesson_id": "L22"
    },
    {
      "title": "Use the readiness calculator",
      "purpose": "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
      "user_action": "Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "studio.html",
        "rubric.json",
        "versions.json",
        "metrics.json"
      ],
      "expected_result": "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
      "resources": [
        "studio.html",
        "rubric.json",
        "versions.json",
        "metrics.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Use the readiness calculator",
        "success_condition": "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
        "how_to_verify": "Record a downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify use the readiness calculator?",
          "answer": "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use studio.html, rubric.json, versions.json, metrics.json. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to use the readiness calculator. A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
        "demo": "Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate use the readiness calculator",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100. A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
        "on_screen_text": [
          "Use the readiness calculator",
          "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit."
        ],
        "screen_recording_instructions": [
          "Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100.",
          "Inspect the result: A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Open studio.html and score each dimension from 0 to 10 with a written reason. Download versions.json and rubric.json; replace the matching extracted files after reviewing them. Leave metrics.json NOT RUN unless you have actual observations. Facebook uses eleven dimensions normalized by dividing by 110 and multiplying by 100.",
          "A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit."
        ]
      },
      "step_id": "L23-S01",
      "lesson_id": "L23"
    },
    {
      "title": "Hash and lint the reviewed files",
      "purpose": "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
      "user_action": "Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "command",
      "commands": {
        "windows": "python media_pack.py hash\npython media_pack.py lint\npython media_pack.py score",
        "macos": "python3 media_pack.py hash\npython3 media_pack.py lint\npython3 media_pack.py score",
        "linux": "python3 media_pack.py hash\npython3 media_pack.py lint\npython3 media_pack.py score"
      },
      "command_type": "shell",
      "required_inputs": [
        "media_pack.py",
        "agent-card.json"
      ],
      "expected_result": "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
      "resources": [
        "media_pack.py",
        "agent-card.json"
      ],
      "checkpoint": {
        "what_is_being_checked": "Hash and lint the reviewed files",
        "success_condition": "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
        "how_to_verify": "Record hASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [
          "MEDIA PASS"
        ],
        "user_visible_success_message": "Evidence saved. HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify hash and lint the reviewed files?",
          "answer": "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use media_pack.py, agent-card.json. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to hash and lint the reviewed files. HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
        "demo": "Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate hash and lint the reviewed files",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again. HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
        "on_screen_text": [
          "Hash and lint the reviewed files",
          "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric."
        ],
        "screen_recording_instructions": [
          "Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again.",
          "Inspect the result: HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Review your changes first. Run the hash command, then lint and score. Inspect report.json. Commands use the current extracted resource folder; PowerShell runs python, macOS zsh and Linux bash run python3. A missing field must be corrected before hashing again.",
          "HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric."
        ]
      },
      "step_id": "L23-S02",
      "lesson_id": "L23"
    },
    {
      "title": "Prove changed bytes and broken content fail",
      "purpose": "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
      "user_action": "Change versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "command",
      "commands": {
        "windows": "python media_pack.py lint\n# After correcting and reviewing the topic:\npython media_pack.py hash\npython media_pack.py lint",
        "macos": "python3 media_pack.py lint\n# After correcting and reviewing the topic:\npython3 media_pack.py hash\npython3 media_pack.py lint",
        "linux": "python3 media_pack.py lint\n# After correcting and reviewing the topic:\npython3 media_pack.py hash\npython3 media_pack.py lint"
      },
      "command_type": "shell",
      "required_inputs": [
        "versions.json",
        "media_pack.py"
      ],
      "expected_result": "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
      "resources": [
        "versions.json",
        "media_pack.py"
      ],
      "checkpoint": {
        "what_is_being_checked": "Prove changed bytes and broken content fail",
        "success_condition": "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
        "how_to_verify": "Record a mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [
          "MEDIA PASS"
        ],
        "user_visible_success_message": "Evidence saved. A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify prove changed bytes and broken content fail?",
          "answer": "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use versions.json, media_pack.py. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to prove changed bytes and broken content fail. A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
        "demo": "Change versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate prove changed bytes and broken content fail",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Change versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification. A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
        "on_screen_text": [
          "Prove changed bytes and broken content fail",
          "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS."
        ],
        "screen_recording_instructions": [
          "Change versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification.",
          "Inspect the result: A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Change versions.json B.topic to a different topic. Run lint and retain MEDIA FAIL. Restore the same topic, review the repair, hash and lint again. Do not weaken the checker or present a structural pass as factual/media verification.",
          "A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS."
        ]
      },
      "step_id": "L23-S03",
      "lesson_id": "L23"
    },
    {
      "title": "Run a source-linked retrieval probe",
      "purpose": "The relevant query returns source-linked chunks; the unrelated query abstains.",
      "user_action": "Run the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "command",
      "commands": {
        "windows": "python media_pack.py retrieve --query \"What makes project evidence useful in a behavioral interview?\"\npython media_pack.py retrieve --query \"quasar waffle weather\"",
        "macos": "python3 media_pack.py retrieve --query \"What makes project evidence useful in a behavioral interview?\"\npython3 media_pack.py retrieve --query \"quasar waffle weather\"",
        "linux": "python3 media_pack.py retrieve --query \"What makes project evidence useful in a behavioral interview?\"\npython3 media_pack.py retrieve --query \"quasar waffle weather\""
      },
      "command_type": "shell",
      "required_inputs": [
        "media_pack.py",
        "rag.jsonl"
      ],
      "expected_result": "The relevant query returns source-linked chunks; the unrelated query abstains.",
      "resources": [
        "media_pack.py",
        "rag.jsonl"
      ],
      "checkpoint": {
        "what_is_being_checked": "Run a source-linked retrieval probe",
        "success_condition": "The relevant query returns source-linked chunks; the unrelated query abstains.",
        "how_to_verify": "Record the relevant query returns source-linked chunks; the unrelated query abstains. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The relevant query returns source-linked chunks; the unrelated query abstains.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. The relevant query returns source-linked chunks; the unrelated query abstains.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify run a source-linked retrieval probe?",
          "answer": "The relevant query returns source-linked chunks; the unrelated query abstains. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use media_pack.py, rag.jsonl. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to run a source-linked retrieval probe. The relevant query returns source-linked chunks; the unrelated query abstains.",
        "demo": "Run the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate run a source-linked retrieval probe",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Run the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified. The relevant query returns source-linked chunks; the unrelated query abstains.",
        "on_screen_text": [
          "Run a source-linked retrieval probe",
          "The relevant query returns source-linked chunks; the unrelated query abstains."
        ],
        "screen_recording_instructions": [
          "Run the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified.",
          "Inspect the result: The relevant query returns source-linked chunks; the unrelated query abstains.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "The relevant query returns source-linked chunks; the unrelated query abstains."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Run the supplied question, then an unrelated query. Inspect the answer and source. This standard-library demo uses lexical word overlap, not embeddings, a vector database or an AI citation measurement. Save actual OS command/exit/output and mark other OS branches unverified.",
          "The relevant query returns source-linked chunks; the unrelated query abstains."
        ]
      },
      "step_id": "L24-S01",
      "lesson_id": "L24"
    },
    {
      "title": "Record provenance and a human approval decision",
      "purpose": "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
      "user_action": "Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision.",
      "explanation": "Keep the input and the observed result together so you can reproduce this action later.",
      "estimated_time": "4 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "provenance-notes.md",
        "agent-card.json",
        "capstone.md"
      ],
      "expected_result": "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
      "resources": [
        "provenance-notes.md",
        "agent-card.json",
        "capstone.md"
      ],
      "checkpoint": {
        "what_is_being_checked": "Record provenance and a human approval decision",
        "success_condition": "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
        "how_to_verify": "Record the handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify record provenance and a human approval decision?",
          "answer": "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use provenance-notes.md, agent-card.json, capstone.md. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to record provenance and a human approval decision. The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
        "demo": "Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate record provenance and a human approval decision",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision. The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
        "on_screen_text": [
          "Record provenance and a human approval decision",
          "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision."
        ],
        "screen_recording_instructions": [
          "Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision.",
          "Inspect the result: The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Use provenance-notes.md to record author, capture/source dates, rights, AI assistance and unresolved claims. SHA256 detects byte changes but does not sign the file or prove truth. Read the source/command cards, review the native capstone and export course evidence. Actual publication requires your own account, current rules and a human decision.",
          "The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision."
        ]
      },
      "step_id": "L24-S02",
      "lesson_id": "L24"
    },
    {
      "title": "Write a repeatable onboarding checklist",
      "purpose": "The September 23 BuildBotsBetter profile wipe removed Desktop, Documents and hidden agent memory together. Repeatable onboarding protects the knowledge needed to recover.",
      "user_action": "Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project.",
      "explanation": "The September 23 BuildBotsBetter profile wipe removed Desktop, Documents and hidden agent memory together. Repeatable onboarding protects the knowledge needed to recover.",
      "estimated_time": "6 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "onboarding-checklist.md"
      ],
      "expected_result": "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
      "resources": [
        "onboarding-checklist.md"
      ],
      "checkpoint": {
        "what_is_being_checked": "Write a repeatable onboarding checklist",
        "success_condition": "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
        "how_to_verify": "Record the checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify write a repeatable onboarding checklist?",
          "answer": "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use onboarding-checklist.md. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to write a repeatable onboarding checklist. The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
        "demo": "Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate write a repeatable onboarding checklist",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project. The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
        "on_screen_text": [
          "Write a repeatable onboarding checklist",
          "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification."
        ],
        "screen_recording_instructions": [
          "Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project.",
          "Inspect the result: The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Fill in onboarding-checklist.md for your agent. Record AGENTS.md, the tool’s user settings and memory folder, the assigned workspace, permissions, guard location, backup schedule, and a final proof task. Keep the checklist in your project.",
          "The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification."
        ]
      },
      "step_id": "L25-S01",
      "lesson_id": "L25"
    },
    {
      "title": "Demonstrate the variable-path limit safely",
      "purpose": "A command-text hook can miss a target computed inside a script or variable. This demonstrator checks resolved paths but is not an operating-system sandbox. For a real operation, enforce the resolved boundary at the operation itself; symlinks and races still require care.",
      "user_action": "Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text.",
      "explanation": "A command-text hook can miss a target computed inside a script or variable. This demonstrator checks resolved paths but is not an operating-system sandbox. For a real operation, enforce the resolved boundary at the operation itself; symlinks and races still require care.",
      "estimated_time": "6 min",
      "action_type": "command",
      "commands": {
        "windows": "python workspace_guard.py --self-test",
        "macos": "python3 workspace_guard.py --self-test",
        "linux": "python3 workspace_guard.py --self-test"
      },
      "command_type": "shell",
      "required_inputs": [
        "workspace_guard.py"
      ],
      "expected_result": "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
      "resources": [
        "workspace_guard.py"
      ],
      "checkpoint": {
        "what_is_being_checked": "Demonstrate the variable-path limit safely",
        "success_condition": "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
        "how_to_verify": "Record gUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
          "I can repeat this action from the saved input."
        ],
        "tokens": [
          "GUARD PASS"
        ],
        "user_visible_success_message": "Evidence saved. GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify demonstrate the variable-path limit safely?",
          "answer": "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use workspace_guard.py. Work in the same practice folder."
        }
      ],
      "quiz": {
        "question": "Does a command-text hook guarantee safety for a path computed inside a script?",
        "answer_type": "single_choice",
        "choices": [
          "No; add enforcement at the resolved operation boundary and independent recovery.",
          "Yes, because the command looks harmless."
        ],
        "correct_answer": 0,
        "explanation": "The profile-wipe failure can involve a runtime variable whose value the text hook cannot see."
      },
      "coach": {
        "intro": "We are going to demonstrate the variable-path limit safely. GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
        "demo": "Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate demonstrate the variable-path limit safely",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text. GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
        "on_screen_text": [
          "Demonstrate the variable-path limit safely",
          "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed."
        ],
        "screen_recording_instructions": [
          "Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text.",
          "Inspect the result: GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Run workspace_guard.py --self-test. Read the source: the target is resolved before deciding. The variable_target case points at the home folder but performs no filesystem operation. Compare this with a hook that sees only command text.",
          "GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed."
        ]
      },
      "step_id": "L25-S02",
      "lesson_id": "L25"
    },
    {
      "title": "Verify the actual protection in your tool",
      "purpose": "A broken hook that blocks everything can look like protection. Some tools fail open when hooks fail. Codex hook changes need review/trust; Grok Build needs its own deny protocol; Antigravity uses a different JSON protocol. Never treat a prompt rule or an inactive hook as enforcement.",
      "user_action": "For CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups.",
      "explanation": "A broken hook that blocks everything can look like protection. Some tools fail open when hooks fail. Codex hook changes need review/trust; Grok Build needs its own deny protocol; Antigravity uses a different JSON protocol. Never treat a prompt rule or an inactive hook as enforcement.",
      "estimated_time": "6 min",
      "action_type": "inspect",
      "commands": {
        "all": ""
      },
      "command_type": "shell",
      "required_inputs": [
        "workspace_guard.py",
        "onboarding-checklist.md"
      ],
      "expected_result": "A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
      "resources": [
        "workspace_guard.py",
        "onboarding-checklist.md"
      ],
      "checkpoint": {
        "what_is_being_checked": "Verify the actual protection in your tool",
        "success_condition": "A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
        "how_to_verify": "Record a harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The harmless command succeeds.",
          "The boundary dry-run denies a profile target.",
          "I recorded the tool’s active hook state, or explicitly recorded that this tool has no working local guard."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I install a guard for this tool?",
          "answer": "Use the linked BBB safety reference for the exact tool/version protocol. Merge into the existing hooks instead of overwriting settings. Review/trust the hook if required, then verify a harmless call and a denied dry-run. The workspace_guard.py file is a teaching demonstration, not a universal hook adapter."
        },
        {
          "question": "Does Grok Bot have a working local delete guard?",
          "answer": "The project’s live September 29 tests found no working shell hook. Restrict the tasks and workspace you hand it, keep independent backups, and do not claim local deletion is guarded."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to verify the actual protection in your tool. A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
        "demo": "For CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate verify the actual protection in your tool",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "For CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups. A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
        "on_screen_text": [
          "Verify the actual protection in your tool",
          "A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation."
        ],
        "screen_recording_instructions": [
          "For CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups.",
          "Inspect the result: A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "For CLI tools, open the hook/permissions screen and confirm the configured guard is active. Use your tool’s documented pre-command hook protocol; the BBB working reference is linked in Sources. Run a harmless directory listing, then a guard dry-run targeting the profile. Do not execute a destructive profile probe. Grok Bot has no proven local shell guard here: use project-only tasks and reviewed actions, with backups.",
          "A harmless command runs, the guard dry-run returns DENY, and the checklist records the actual hook protocol and active state or the Grok Bot limitation."
        ]
      },
      "step_id": "L25-S03",
      "lesson_id": "L25"
    },
    {
      "title": "Enable a real backup of memory and project files",
      "purpose": "System Restore alone is not a personal-file backup. Previous Versions can disappear as shadow storage fills; OneDrive’s common-folder backup omits hidden agent folders.",
      "user_action": "Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials.",
      "explanation": "System Restore alone is not a personal-file backup. Previous Versions can disappear as shadow storage fills; OneDrive’s common-folder backup omits hidden agent folders.",
      "estimated_time": "6 min",
      "action_type": "command",
      "commands": {
        "windows": ".\\profile-backup.ps1 -Destination D:\\Backups\\llm-profiles",
        "macos": "bash profile-backup.sh /Volumes/YourBackup/llm-profiles",
        "linux": "bash profile-backup.sh /media/YourBackup/llm-profiles"
      },
      "command_type": "shell",
      "required_inputs": [
        "profile-backup.ps1",
        "profile-backup.sh",
        "onboarding-checklist.md"
      ],
      "expected_result": "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
      "resources": [
        "profile-backup.ps1",
        "profile-backup.sh",
        "onboarding-checklist.md"
      ],
      "checkpoint": {
        "what_is_being_checked": "Enable a real backup of memory and project files",
        "success_condition": "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
        "how_to_verify": "Record a backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The destination is a mounted second drive.",
          "The hidden memory/settings folders and project files are included.",
          "An automated backup is enabled and its latest successful run is recorded."
        ],
        "tokens": [],
        "user_visible_success_message": "Evidence saved. A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The backup drive is missing or the script reports a failure",
          "likely_cause": "The destination may be on the same volume, unmounted, or unwritable.",
          "diagnostic_question": "Can you create a scratch file on the actual second drive?",
          "fix": "Mount the drive and choose an accessible backup directory. Inspect every error; robocopy codes 8 and higher are failures.",
          "alternative_fix": "Use the operating system backup application and verify the same folder coverage and successful run.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Mount the drive and choose an accessible backup directory. Inspect every error; robocopy codes 8 and higher are failures."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify enable a real backup of memory and project files?",
          "answer": "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use profile-backup.ps1, profile-backup.sh, onboarding-checklist.md. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to enable a real backup of memory and project files. A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
        "demo": "Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Mount the drive and choose an accessible backup directory. Inspect every error; robocopy codes 8 and higher are failures.",
        "help": "Can you create a scratch file on the actual second drive?"
      },
      "video": {
        "purpose": "Demonstrate enable a real backup of memory and project files",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials. A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
        "on_screen_text": [
          "Enable a real backup of memory and project files",
          "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded."
        ],
        "screen_recording_instructions": [
          "Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials.",
          "Inspect the result: A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Connect a second backup drive. Windows: configure System Protection as additional recovery and use a scheduled copy/File History that explicitly includes hidden agent folders. macOS: configure Time Machine and verify dot folders are included. Linux: use a home backup such as restic/Deja Dup; Timeshift omits /home by default. Download the copy-only profile backup script for your platform, inspect it, and adapt the folder list to your tool. Record the last successful scheduled run; protect backup access because it may contain credentials.",
          "A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded."
        ]
      },
      "step_id": "L25-S04",
      "lesson_id": "L25"
    },
    {
      "title": "Practice a hash-verified restore",
      "purpose": "A backup becomes useful only when restoration has been practiced. A same-volume practice copy proves the procedure but not protection against disk failure.",
      "user_action": "Run restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile.",
      "explanation": "A backup becomes useful only when restoration has been practiced. A same-volume practice copy proves the procedure but not protection against disk failure.",
      "estimated_time": "6 min",
      "action_type": "command",
      "commands": {
        "windows": "python restore_drill.py --backup-root D:\\Backups\\bbb-practice --work-root practice",
        "macos": "python3 restore_drill.py --backup-root /Volumes/YourBackup/bbb-practice --work-root practice",
        "linux": "python3 restore_drill.py --backup-root /media/YourBackup/bbb-practice --work-root practice"
      },
      "command_type": "shell",
      "required_inputs": [
        "restore_drill.py"
      ],
      "expected_result": "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
      "resources": [
        "restore_drill.py"
      ],
      "checkpoint": {
        "what_is_being_checked": "Practice a hash-verified restore",
        "success_condition": "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
        "how_to_verify": "Record rESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch. Include the actual result or a specific observation; do not paste credentials.",
        "criteria": [
          "The proof file SHA256 hashes match.",
          "I restored and opened a non-secret memory/settings file from my real backup.",
          "The restored file is in scratch; the live profile was not overwritten."
        ],
        "tokens": [
          "RESTORE PASS"
        ],
        "user_visible_success_message": "Evidence saved. RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
        "failure_message": "Compare the observed output with the required result before continuing.",
        "troubleshooting_path": "Open the help for this step."
      },
      "troubleshooting": [
        {
          "problem": "The result differs from the checkpoint",
          "likely_cause": "You may be in a different folder or using a different input/version.",
          "diagnostic_question": "What is the current directory, exact command, exit code and complete output?",
          "fix": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
          "alternative_fix": "Open the supplied resource and inspect the input manually; use the expected-result example to narrow the difference.",
          "when_to_escalate": "Stop after two explained attempts if the problem needs account access, permissions or changes outside this workspace. Share redacted evidence.",
          "bot_coach_response": "Compare the input filename and working directory with this step. Rerun only after correcting that difference."
        }
      ],
      "faqs": [
        {
          "question": "How do I verify practice a hash-verified restore?",
          "answer": "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch. Keep the command or observation with your evidence."
        },
        {
          "question": "Which files do I need for this step?",
          "answer": "Use restore_drill.py. Work in the same practice folder."
        }
      ],
      "quiz": null,
      "coach": {
        "intro": "We are going to practice a hash-verified restore. RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
        "demo": "Run restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile.",
        "handoff": "Try this in your own practice workspace, then compare the result.",
        "success": "Keep that result with your evidence.",
        "failure": "Compare the input filename and working directory with this step. Rerun only after correcting that difference.",
        "help": "What is the current directory, exact command, exit code and complete output?"
      },
      "video": {
        "purpose": "Demonstrate practice a hash-verified restore",
        "duration_target": "45–75 seconds",
        "visual_type": "step walkthrough with optional browser narration; no rendered video is claimed",
        "narration_script": "Run restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile. RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
        "on_screen_text": [
          "Practice a hash-verified restore",
          "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch."
        ],
        "screen_recording_instructions": [
          "Run restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile.",
          "Inspect the result: RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.",
          "Record the evidence and check each criterion."
        ],
        "animation_instructions": "Highlight the action, then the corresponding result; avoid motion during command copying.",
        "cursor_instructions": "Point to the exact input and checkpoint; do not hide errors.",
        "important_visuals": [
          "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch."
        ],
        "bot_position": "right sidebar",
        "bot_expression": "calm",
        "transition": "Continue after checkpoint",
        "captions": [
          "Run restore_drill.py with a separate backup directory. The script creates a new proof file, copies it, restores a second copy, and compares SHA256 hashes. Then restore one non-secret file from your actual agent memory backup into a separate scratch directory and open it; do not overwrite the live profile.",
          "RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch."
        ]
      },
      "step_id": "L25-S05",
      "lesson_id": "L25"
    }
  ],
  "resources": [
    {
      "resource_id": "R001",
      "name": "workspace_guard.py",
      "type": "py",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "# VERSION HISTORY\n# 2026-10-05 EDT | chatgpt_codex | overnight-catalog | v1: learner resource.\n\"\"\"Dry-run resolved-path boundary demonstration. This script never deletes anything.\"\"\"\nimport argparse\nfrom pathlib import Path\n\ndef permitted(target, workspace):\n    root = Path(workspace).resolve()\n    candidate = Path(target).resolve()\n    home = Path.home().resolve()\n    if root == home or root == root.anchor or root in home.parents:\n        return False\n    return candidate != root and root in candidate.parents and candidate != home and candidate not in home.parents\n\ndef main():\n    p=argparse.ArgumentParser(description=__doc__)\n    p.add_argument('--workspace',default='practice')\n    p.add_argument('--target')\n    p.add_argument('--self-test',action='store_true')\n    a=p.parse_args()\n    root=Path(a.workspace).resolve()\n    if a.self_test:\n        assert permitted(root/'proof.txt',root)\n        assert not permitted(Path.home(),root)\n        assert not permitted(root.parent/'outside.txt',root)\n        assert not permitted(root,root)\n        variable_target=Path.home()\n        assert not permitted(variable_target,root)\n        print('GUARD PASS: inside allowed; profile, variable-derived profile, outside, workspace root denied; no deletion executed')\n    else:\n        if not a.target:p.error('--target required')\n        allowed=permitted(a.target,a.workspace)\n        print(('ALLOW' if allowed else 'DENY')+': resolved boundary; dry-run only')\n        raise SystemExit(0 if allowed else 2)\nif __name__=='__main__':main()\n",
      "filename": "workspace_guard.py",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R002",
      "name": "restore_drill.py",
      "type": "py",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "# VERSION HISTORY\n# 2026-10-05 EDT | chatgpt_codex | overnight-catalog | v1: learner resource.\n\"\"\"Backup and restore a new proof file only; never edits an existing file or profile.\"\"\"\nimport argparse,hashlib,json,logging\nfrom datetime import datetime,timezone\nfrom pathlib import Path\nimport shutil\ndef digest(p):return hashlib.sha256(p.read_bytes()).hexdigest()\ndef main():\n    a=argparse.ArgumentParser(description=__doc__)\n    a.add_argument('--backup-root',required=True)\n    a.add_argument('--work-root',default='practice')\n    x=a.parse_args()\n    run=datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%S%fZ')\n    work=Path(x.work_root).resolve()/('restore-'+run)\n    backup=Path(x.backup_root).resolve()/('restore-'+run)\n    if work==backup or work in backup.parents or backup in work.parents:raise SystemExit('Use separate, non-overlapping work and backup roots')\n    work.mkdir(parents=True,exist_ok=False);backup.mkdir(parents=True,exist_ok=False)\n    logging.basicConfig(filename=work/'drill.log',encoding='utf-8',level=logging.INFO)\n    source=work/'proof.txt';source.write_text('BuildBotsBetter restore proof '+run,encoding='utf-8')\n    shutil.copy2(source,backup/'proof.txt')\n    restored=work/'restored.txt';shutil.copy2(backup/'proof.txt',restored)\n    assert digest(source)==digest(restored)\n    receipt={'result':'RESTORE PASS','source_sha256':digest(source),'restored_sha256':digest(restored),'backup_root':str(backup),'off_volume':work.anchor.lower()!=backup.anchor.lower(),'scope':'new proof file only; does not establish a scheduled profile backup'}\n    (work/'receipt.json').write_text(json.dumps(receipt,indent=2),encoding='utf-8')\n    logging.info('Proof backup and hash-verified restore completed')\n    print(json.dumps(receipt,indent=2))\n    if not receipt['off_volume']:print('SAME VOLUME: practice passed; configure a second drive for disaster recovery')\nif __name__=='__main__':main()\n",
      "filename": "restore_drill.py",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R003",
      "name": "onboarding-checklist.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "# Repeatable agent onboarding\nagent:\ntool version:\nrules file:\nmemory/settings location:\nassigned workspace:\npermissions / actions needing approval:\nhook location and protocol:\nguard active (harmless command + denied dry-run):\nvariable-derived path limit understood:\nbackup destination (second drive):\nbackup schedule / last successful run:\nrestored memory file / matching SHA256:\nfinal task / evidence / reviewer:\n\nProtect memory/settings and project files. A prompt rule is not an operating-system boundary.\nA command-text hook can miss a path computed in a script or variable. Resolve paths at the\noperation boundary and fail closed; keep independent backups and monitoring.\nWindows System Restore reverts system components, not a reliable personal-file backup.\nOneDrive folder backup does not automatically include hidden agent folders.\nmacOS: check Time Machine includes hidden agent folders. Linux: Timeshift excludes /home by\ndefault; use a separate home backup or explicitly include it.\n",
      "filename": "onboarding-checklist.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R004",
      "name": "profile-backup.ps1",
      "type": "ps1",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "# VERSION HISTORY\n# 2026-10-05 EDT | chatgpt_codex | overnight-catalog | v1: copy-only explicit profile backup.\nparam([Parameter(Mandatory=$true)][string]$Destination,[string]$ProjectPath=(Get-Location).Path)\n$ErrorActionPreference='Stop'\n$destRoot=[IO.Path]::GetFullPath($Destination)\n$profileRoot=[IO.Path]::GetFullPath($env:USERPROFILE)\nif ([IO.Path]::GetPathRoot($destRoot) -eq [IO.Path]::GetPathRoot($profileRoot)) { throw 'Choose a backup destination on a second drive.' }\nif (-not (Test-Path -LiteralPath ([IO.Path]::GetPathRoot($destRoot)))) { throw 'Backup drive is unavailable.' }\n$runDir=Join-Path $destRoot (Get-Date -Format 'yyyyMMdd-HHmmss-fff')\nNew-Item -ItemType Directory -Path $runDir -ErrorAction Stop | Out-Null\nforeach ($folderName in @('.codex','.claude','.grok','.qwen','.gemini')) {\n  $sourceDir=Join-Path $profileRoot $folderName\n  if (Test-Path -LiteralPath $sourceDir) {\n    $copyDir=Join-Path $runDir $folderName\n    & robocopy $sourceDir $copyDir /E /XJ /R:1 /W:1 /COPY:DAT /DCOPY:DAT /NP\n    if ($LASTEXITCODE -ge 8) { throw \"Backup failed for $folderName (robocopy $LASTEXITCODE).\" }\n  }\n}\n$projectSource=[IO.Path]::GetFullPath($ProjectPath)\nif (-not (Test-Path -LiteralPath $projectSource -PathType Container)) { throw 'Project directory is unavailable.' }\n& robocopy $projectSource (Join-Path $runDir 'project') /E /XJ /R:1 /W:1 /COPY:DAT /DCOPY:DAT /NP\nif ($LASTEXITCODE -ge 8) { throw \"Project backup failed (robocopy $LASTEXITCODE).\" }\n\"PROFILE BACKUP PASS: $runDir\"\n# Copy-only: no /MIR, /PURGE or deletion. Backup may contain credentials; protect the drive.\n# Add the Grok Bot profile and project path explicitly after finding them in your own installation.\n",
      "filename": "profile-backup.ps1",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R005",
      "name": "profile-backup.sh",
      "type": "sh",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "#!/usr/bin/env bash\n# VERSION HISTORY\n# 2026-10-05 EDT | chatgpt_codex | overnight-catalog | v1: copy-only explicit profile backup.\nset -euo pipefail\ndest=${1:?Pass a mounted external-drive backup directory}\nproject=${2:-$PWD}\nmkdir -p \"$dest\"\nif [ \"$(df -P \"$HOME\" | tail -1 | awk '{print $1}')\" = \"$(df -P \"$dest\" | tail -1 | awk '{print $1}')\" ]; then\n  echo 'Choose a destination on another mounted filesystem.' >&2; exit 2\nfi\nrun=\"$dest/$(date -u +%Y%m%dT%H%M%SZ)-$$\"\nmkdir \"$run\"\nfor name in .codex .claude .grok .qwen .gemini; do\n  if [ -d \"$HOME/$name\" ]; then cp -pR \"$HOME/$name\" \"$run/$name\"; fi\ndone\nif [ ! -d \"$project\" ]; then echo 'Project directory is unavailable.' >&2; exit 2; fi\ncp -pR \"$project\" \"$run/project\"\necho \"PROFILE BACKUP PASS: $run\"\n# This backup can contain credentials; protect access to the backup drive.\n",
      "filename": "profile-backup.sh",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R006",
      "name": "media_pack.py",
      "type": "py",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "# VERSION HISTORY\n# 2026-10-06 EDT | chatgpt_codex | media-catchup | v1: created structural draft checks, normalized scoring and lexical retrieval.\n\"\"\"Check local draft structure, provenance and retrieval. Never publishes content.\"\"\"\nimport argparse, hashlib, json, logging, math, re\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nROOT=Path(__file__).resolve().parent\ndef read(name):return json.loads((ROOT/name).read_text(encoding='utf-8'))\ndef digest(p):return hashlib.sha256(p.read_bytes()).hexdigest()\ndef payloads():return sorted(p for p in ROOT.iterdir() if p.is_file() and p.name not in ['provenance.json','report.json','media-pack.log'] and not p.name.endswith('.zip'))\ndef issues():\n    problems=[]\n    try:\n        k=read('knowledge.json');versions=read('versions.json');cards=read('agent-card.json');rubric=read('rubric.json')\n        for field in ['topic','question','answer','context','entities','sources','author','date','next_action']:\n            if not k.get(field):problems.append('Missing knowledge field: '+field)\n        if not all(re.match(r'^https://[^/]+/.+',s) for s in k.get('sources',[])):problems.append('Source must be a direct HTTPS page')\n        if not all(e.casefold() in k['answer'].casefold() for e in k['entities']):problems.append('Answer lacks a named entity')\n        for entity in k['entities']:\n            if entity.casefold() not in versions['B']['text'].casefold():problems.append('Version B lacks entity '+entity)\n        if versions['A']['topic']!=versions['B']['topic'] or versions['A']['topic']!=k['topic']:problems.append('Comparison topic changed')\n        if versions['A']['format']!=versions['B']['format']:problems.append('Comparison format changed')\n        for version in versions.values():\n            if re.search(r'guarantee[sd]? (?:AI training|viral|ranking)',version['text'],re.I):problems.append('Unjustified outcome guarantee')\n            if not version['text'].strip():problems.append('Empty comparison')\n        if k['platform']=='threads' and any(len(v['text'])>500 for v in versions.values()):problems.append('Threads root draft exceeds 500 characters')\n        if k['platform']=='xai-xsi' and any(len(v['text'])>280 for v in versions.values()):problems.append('X practice root draft exceeds 280 characters')\n        if k['platform']=='pinterest':\n            pin=read('native.json')\n            if len(pin['title'])>100 or len(pin['description'])>500:problems.append('Pin practice metadata exceeds title/description limit')\n            if pin['ratio']!='2:3' or not pin['board'] or not pin['alt']:problems.append('Incomplete static Pin card')\n        for osname in ['windows','macos','linux']:\n            card=cards.get(osname,{})\n            if not all(card.get(f) is not None for f in ['shell','cwd','prerequisites','command','expected_exit','expected_output','failure','scope']):problems.append('Incomplete command card '+osname)\n            if card.get('expected_exit')!=0:problems.append('Unexpected success exit '+osname)\n        rows=[json.loads(line) for line in (ROOT/'rag.jsonl').read_text(encoding='utf-8').splitlines() if line.strip()]\n        if len({r['id'] for r in rows})!=len(rows) or len(rows)<2:problems.append('RAG IDs duplicate or too few rows')\n        for r in rows:\n            if not all(r.get(f) for f in ['id','entity','question','answer','source']):problems.append('Incomplete retrieval row')\n            if r['entity'].casefold() not in r['answer'].casefold():problems.append('Retrieval answer not standalone')\n        score(rubric)\n        manifest=read('provenance.json')\n        actual={p.name:digest(p) for p in payloads()}\n        if manifest['sha256']!=actual:problems.append('Changed bytes or missing provenance entries; review before hash')\n    except (KeyError,ValueError,TypeError,OSError) as e:problems.append('Invalid input: '+str(e))\n    return problems\ndef score(rubric):\n    values=rubric['values'];names=rubric['dimensions']\n    if len(values)!=len(names) or not values:raise ValueError('Rubric dimensions/values mismatch')\n    if any(isinstance(v,bool) or not isinstance(v,(int,float)) or not math.isfinite(v) or not 0<=v<=10 for v in values):raise ValueError('Scores must be finite numbers from 0 to 10')\n    return round(100*sum(values)/(10*len(values)),2)\ndef main():\n    logging.basicConfig(filename=ROOT/'media-pack.log',encoding='utf-8',level=logging.INFO)\n    parser=argparse.ArgumentParser(description=__doc__);parser.add_argument('action',choices=['lint','hash','score','retrieve']);parser.add_argument('--query',default='');a=parser.parse_args()\n    if a.action=='hash':\n        data={'at':datetime.now(timezone.utc).isoformat(),'author':'learner draft','kind':'unsigned SHA256 integrity manifest; not C2PA or proof of truth','sha256':{p.name:digest(p) for p in payloads()}}\n        (ROOT/'provenance.json').write_text(json.dumps(data,indent=2),encoding='utf-8');print('HASH PASS: unsigned reviewed bytes')\n    elif a.action=='lint':\n        errors=issues();report={'result':'MEDIA FAIL' if errors else 'MEDIA PASS','issues':errors,'scope':'Structural local draft checks; no factual, publication, ranking, OCR/ASR or AI-training certification'}\n        (ROOT/'report.json').write_text(json.dumps(report,indent=2),encoding='utf-8');print(json.dumps(report,indent=2));logging.info('%s issues=%d',report['result'],len(errors));raise SystemExit(1 if errors else 0)\n    elif a.action=='score':print('READINESS '+str(score(read('rubric.json')))+' /100: subjective rubric; no distribution prediction')\n    else:\n        query=set(re.findall(r'\\w+',a.query.casefold()));rows=[json.loads(s) for s in (ROOT/'rag.jsonl').read_text(encoding='utf-8').splitlines() if s.strip()]\n        ranked=sorted([(len(query & set(re.findall(r'\\w+',r['question'].casefold()))),r) for r in rows],key=lambda x:x[0],reverse=True)\n        hits=[r for n,r in ranked if n>0][:2];print(json.dumps({'retrieval':'lexical demonstration, not vector embeddings','matches':hits,'abstained':not bool(hits)},indent=2))\nif __name__=='__main__':\n    try:main()\n    except Exception:logging.exception('Media pack failed');raise\n",
      "filename": "media_pack.py",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R007",
      "name": "studio.html",
      "type": "html",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "<!doctype html>\n<!-- VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original local native draft editor and score calculator.\n-->\n<html lang=\"en\"><meta charset=\"utf-8\"><meta name=\"viewport\" content=\"width=device-width,initial-scale=1\"><title>Native draft studio</title>\n<style>body{font:17px system-ui,sans-serif;background:#10151d;color:#f4f6fb;margin:0;padding:24px;max-width:1000px}textarea,input{font:inherit;background:#202a38;color:inherit;border:1px solid #687789;border-radius:5px;box-sizing:border-box;width:100%;padding:12px}textarea{min-height:160px}section{padding:18px;border:1px solid #687789;border-radius:8px;margin:16px 0}button{padding:12px;margin:8px;background:#b7f48a;border:0;border-radius:5px;color:#10151d}label{display:block;margin:12px 0}h1{font-size:clamp(26px,6vw,42px)}.pair{display:grid;grid-template-columns:repeat(auto-fit,minmax(min(300px,100%),1fr));gap:16px}small{display:block;color:#bac8d7}output{font-weight:bold}a{color:#b7f48a}</style>\n<h1>LinkedIn draft studio</h1><p>A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.</p><p>One topic, one format, two versions. Keep the audience and promise fixed. These drafts are synthetic practice examples.</p>\n<label>Topic <input id=\"topic\"></label><label>Native format <input id=\"format\"></label>\n<div class=\"pair\"><section><h2>A · Human baseline</h2><textarea id=\"a\"></textarea><small id=\"ac\"></small></section><section><h2>B · Human and machine clarity</h2><textarea id=\"b\"></textarea><small id=\"bc\"></small></section></div>\n<p id=\"difference\"></p><button id=\"export\">Download versions.json</button><p id=\"status\" role=\"status\"></p>\n<section><h2>Readiness self-assessment</h2><p>Rate each dimension from 0 to 10 using your saved evidence. The result measures this rubric, not the chance of virality or AI citation. Facebook's eleven dimensions are normalized to 100.</p><div id=\"scores\"></div><output id=\"total\"></output><button id=\"rubric-export\">Download rubric.json</button></section>\n<section><h2>Review before publication</h2><p>Check factual claims, media consent, community rules, current format limits and destination links. A readable draft does not guarantee model training, indexing or recommendation. This page saves downloads only; it sends nothing to a platform. Return to the course in your other browser tab after downloading your drafts.</p></section>\n<script>\n// VERSION HISTORY\n// 2026-10-06 EDT | chatgpt_codex | media-catchup | v1: local-only editor; no API or storage.\nconst initial={\"A\": {\"topic\": \"Interview project evidence\", \"format\": \"professional text post\", \"text\": \"Interview season is brutal. Telling your story matters so much. What is your best advice for showing up confidently?\"}, \"B\": {\"topic\": \"Interview project evidence\", \"format\": \"professional text post\", \"text\": \"For a behavioral interview, I am turning project evidence into five fields: role, problem, my action, observed result and a supporting artifact. I separate my contribution from the team result and leave unmeasured outcomes unquantified. 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Review and replace the matching file in the extracted resource folder.';}\n$('export').onclick=()=>download('versions.json',Object.fromEntries(['A','B'].map((v,i)=>[v,{topic:$('topic').value,format:$('format').value,text:$(i?'b':'a').value}])));\n$('rubric-export').onclick=()=>download('rubric.json',{dimensions,values:[...document.querySelectorAll('#scores input')].map(x=>Number(x.value)),kind:'self-assessment; review evidence before interpreting'});\nfor(const id of ['a','b'])$(id).addEventListener('input',update);update();\n</script></html>",
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      "contents": "{\n  \"A\": {\n    \"topic\": \"Interview project evidence\",\n    \"format\": \"professional text post\",\n    \"text\": \"Interview season is brutal. Telling your story matters so much. What is your best advice for showing up confidently?\"\n  },\n  \"B\": {\n    \"topic\": \"Interview project evidence\",\n    \"format\": \"professional text post\",\n    \"text\": \"For a behavioral interview, I am turning project evidence into five fields: role, problem, my action, observed result and a supporting artifact. I separate my contribution from the team result and leave unmeasured outcomes unquantified. Which field helps you evaluate a candidate most?\"\n  }\n}\n",
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      "contents": "{\n  \"platform\": \"linkedin\",\n  \"topic\": \"Interview project evidence\",\n  \"question\": \"What makes project evidence useful in a behavioral interview?\",\n  \"answer\": \"Project evidence in a behavioral interview connects the role, problem, personal action, observed result and supporting artifact. A candidate should distinguish their own contribution from team work and leave unmeasured outcomes unquantified.\",\n  \"context\": \"A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\",\n  \"example\": \"Synthetic teaching example; replace with a checked observation before public use.\",\n  \"entities\": [\n    \"project evidence\",\n    \"behavioral interview\"\n  ],\n  \"sources\": [\n    \"https://www.linkedin.com/help/linkedin/answer/a1339724\"\n  ],\n  \"related_topics\": [\n    \"career portfolios\",\n    \"professional identity\"\n  ],\n  \"keywords\": [\n    \"project evidence\",\n    \"behavioral interview\"\n  ],\n  \"language\": \"en\",\n  \"author\": \"chatgpt_codex teaching fixture\",\n  \"date\": \"2026-10-06\",\n  \"next_action\": \"Choose a real project and verify each claimed contribution against an artifact you are allowed to share.\"\n}\n",
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      "contents": "{\"id\": \"linkedin-1\", \"entity\": \"project evidence\", \"question\": \"What makes project evidence useful in a behavioral interview?\", \"answer\": \"Project evidence in a behavioral interview connects the role, problem, personal action, observed result and supporting artifact. A candidate should distinguish their own contribution from team work and leave unmeasured outcomes unquantified.\", \"source\": \"https://www.linkedin.com/help/linkedin/answer/a1339724\"}\n{\"id\": \"linkedin-2\", \"entity\": \"project evidence\", \"question\": \"What should the project evidence audience do next?\", \"answer\": \"project evidence: Choose a real project and verify each claimed contribution against an artifact you are allowed to share.\", \"source\": \"https://www.linkedin.com/help/linkedin/answer/a1339724\"}\n",
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      "contents": "{\n  \"dimensions\": [\n    \"Professional value\",\n    \"Semantic hook\",\n    \"Entity grounding\",\n    \"Keyword consistency\",\n    \"Case evidence\",\n    \"Multimodal alignment\",\n    \"Standalone retrieval\",\n    \"Agent actionability\",\n    \"Provenance\",\n    \"Human signal\"\n  ],\n  \"values\": [\n    0,\n    0,\n    0,\n    0,\n    0,\n    0,\n    0,\n    0,\n    0,\n    0\n  ],\n  \"kind\": \"unscored draft; score only after reviewing evidence\"\n}\n",
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      "contents": "{\n  \"role\": \"candidate, example persona\",\n  \"pillars\": [\n    \"interview preparation\",\n    \"project evidence\",\n    \"peer learning\"\n  ],\n  \"metric_status\": \"unmeasured\",\n  \"format\": \"text\"\n}\n",
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      "contents": "{\n  \"taxonomy\": [\n    \"ESTABLISHED\",\n    \"EVIDENCE-SUPPORTED\",\n    \"EMERGING\",\n    \"HYPOTHESIS\",\n    \"SPECULATION\"\n  ],\n  \"claims\": [\n    {\n      \"claim\": \"LinkedIn describes feed relevance using professional information and member activity.\",\n      \"label\": \"ESTABLISHED\",\n      \"source\": \"https://www.linkedin.com/help/linkedin/answer/a1339724\",\n      \"checked\": \"2026-10-06\"\n    },\n    {\n      \"claim\": \"An evidence-first interview post will elicit more specific hiring feedback than a confidence platitude.\",\n      \"label\": \"HYPOTHESIS\",\n      \"source\": \"Local experiment proposal; no result claimed\"\n    },\n    {\n      \"claim\": \"Future SI may coordinate knowledge across platforms\",\n      \"label\": \"SPECULATION\",\n      \"source\": \"Design possibility; no accepted forecast or training guarantee\"\n    }\n  ]\n}\n",
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      "contents": "{\n  \"status\": \"NOT RUN\",\n  \"topic\": \"Interview project evidence\",\n  \"format\": \"professional text post\",\n  \"primary_metric\": \"qualified professional replies per impression\",\n  \"exposures\": null,\n  \"outcomes\": null,\n  \"rate\": null,\n  \"confounders\": [\n    \"audience mix\",\n    \"time of day\",\n    \"distribution\",\n    \"prior exposure\"\n  ],\n  \"stop_rule\": \"Set a observation window and minimum exposure count before starting. Report inconclusive when underpowered.\",\n  \"ai_citations\": [],\n  \"agent_execution\": {\n    \"attempts\": 0,\n    \"successes\": 0,\n    \"os_evidence\": []\n  }\n}\n",
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      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\nRecord author, real capture date, source URLs, permissions, AI assistance, changes and reviewed evidence. SHA256 detects changes to bytes. A C2PA signed manifest requires separate credentials and verification; no signature is supplied.\nPublic status does not grant universal crawler/API access or guarantee pretraining, fine-tuning, retrieval or AI citation. Synthetic Intelligence (SI) is a speculative course term; semantic indexing is a separate concept.\n",
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      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n",
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      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n",
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      "contents": "{\n  \"schema_version\": 1,\n  \"agent_role\": \"LinkedIn Content Agent\",\n  \"topic\": \"Interview project evidence\",\n  \"operating_points\": {\n    \"01_platform_identity\": \"LinkedIn Content Agent; create native professional text post drafts rather than swapping platform labels.\",\n    \"02_audience\": \"A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\",\n    \"03_content_graph\": {\n      \"format\": \"professional text post\",\n      \"topic\": \"Interview project evidence\",\n      \"alternatives\": \"First native surface/format exercises and native.json.\"\n    },\n    \"04_distribution_graph\": \"Professional connection/follower graph, relevant feed suggestions, documents, reposts and newsletters have distinct paths.\",\n    \"05_algorithm\": {\n      \"source\": \"https://www.linkedin.com/help/linkedin/answer/a1339724\",\n      \"documented_claim\": \"LinkedIn describes feed relevance using professional information and member activity.\",\n      \"private_weights\": \"UNKNOWN; never fabricate weights, thresholds or penalties.\"\n    },\n    \"06_engagement_graph\": {\n      \"actions\": [\n        \"Comment with hiring experience\",\n        \"Repost with a relevant lesson\",\n        \"Follow the evidence series\",\n        \"Visit the permissioned portfolio\"\n      ],\n      \"useful_metric\": \"qualified professional replies per impression\",\n      \"valuation\": \"Predeclare useful outcomes and denominators; no fabricated platform point values.\"\n    },\n    \"07_native_formats\": {\n      \"role\": \"candidate, example persona\",\n      \"pillars\": [\n        \"interview preparation\",\n        \"project evidence\",\n        \"peer learning\"\n      ],\n      \"metric_status\": \"unmeasured\",\n      \"format\": \"text\"\n    },\n    \"08_creative_language\": \"Professional, specific and evidence-led; no fake employers, achievements, endorsements or confidential artifacts.\",\n    \"09_cta_system\": [\n      \"Comment with hiring experience\",\n      \"Repost with a relevant lesson\",\n      \"Follow the evidence series\",\n      \"Visit the permissioned portfolio\"\n    ],\n    \"10_search\": {\n      \"entities\": [\n        \"project evidence\",\n        \"behavioral interview\"\n      ],\n      \"query\": \"What makes project evidence useful in a behavioral interview?\",\n      \"task\": \"Observe current queries and record date/access limits before writing metadata.\"\n    },\n    \"11_platform_ai\": {\n      \"description\": \"LinkedIn’s March 2026 engineering account describes LLM/GPU-based ranking and professional-interest understanding. This supports clear career context, not a guaranteed recruiter result or knowledge of private weights.\",\n      \"source\": \"https://www.linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed\",\n      \"checked\": \"2026-10-06\",\n      \"status\": \"Documented description; actual account availability/execution UNVERIFIED.\"\n    },\n    \"12_ai_discovery\": {\n      \"input_output\": \"Content and professional/member context → feed relevance. AI drafting/hiring tools are separate products with separate access checks.\",\n      \"rule\": \"Do not promise a private model will parse, recommend, cite or train on the object.\"\n    },\n    \"13_multimodal\": {\n      \"entities\": [\n        \"project evidence\",\n        \"behavioral interview\"\n      ],\n      \"task\": \"Match visual labels, speech and captions; retain a text equivalent. Check actual media manually. No invented OCR/ASR results.\"\n    },\n    \"14_community\": {\n      \"task\": \"Review two public examples and current rules; obtain permission for private quotes.\",\n      \"fixture_status\": \"A/B and practice replies are synthetic, not real member observations.\"\n    },\n    \"15_native_patterns\": {\n      \"format\": \"professional text post\",\n      \"hypothesis\": \"An evidence-first interview post will elicit more specific hiring feedback than a confidence platitude.\",\n      \"status\": \"UNTESTED for this draft; empirical superiority not claimed.\"\n    },\n    \"16_failure_modes\": \"Professional, specific and evidence-led; no fake employers, achievements, endorsements or confidential artifacts. Also refuse unsupported source/rights claims and never invent algorithmic penalties.\",\n    \"17_experiments\": {\n      \"controls\": [\n        \"same topic\",\n        \"same audience\",\n        \"same format\",\n        \"same promise\"\n      ],\n      \"protocol\": \"Change one factor; predeclare metric, time window and minimum exposure. Log confounders and inconclusive results.\"\n    },\n    \"18_analytics\": {\n      \"primary_metric\": \"qualified professional replies per impression\",\n      \"observations\": \"NOT RUN; metrics.json uses nulls, not fabricated counts.\"\n    },\n    \"19_generation_rules\": {\n      \"inputs\": [\n        \"knowledge.json\",\n        \"actual audience observations\",\n        \"current rules\",\n        \"format limits\",\n        \"rights/permissions\",\n        \"source dates\"\n      ],\n      \"outputs\": [\n        \"native draft\",\n        \"controlled A/B\",\n        \"media/caption plan\",\n        \"sources and factual review\",\n        \"rubric with reasons\",\n        \"approval request\",\n        \"measurement plan\"\n      ],\n      \"state_machine\": [\n        \"DRAFT\",\n        \"REVIEW_REQUESTED\",\n        \"APPROVED_BY_AUTHORIZED_HUMAN\",\n        \"PUBLISHED_BY_AUTHORIZED_HUMAN\",\n        \"MEASURED\",\n        \"CORRECTED\"\n      ],\n      \"stop_conditions\": [\n        \"Missing audience/source\",\n        \"Unknown media rights\",\n        \"Unsupported claim\",\n        \"Unverified current publishing limit\",\n        \"No publication authorization\"\n      ],\n      \"scope\": \"Offline authoring and local validation only; no account connection or publishing tool provided.\"\n    },\n    \"20_b3_knowledge_layer\": {\n      \"artifacts\": [\n        \"knowledge.json\",\n        \"rag.jsonl\",\n        \"claims.json\",\n        \"agent-card.json\",\n        \"provenance-notes.md\"\n      ],\n      \"limits\": \"Lexical demonstration, unsigned integrity hashes, no guaranteed model training or future SI coordination.\"\n    }\n  },\n  \"source_scope\": \"Primary documentation and original editorial design. Feature description is not account-execution evidence.\"\n}\n",
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      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n",
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      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn Content Agent operating manual\n\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nRead all twenty points in agent-training.json before drafting. Preserve concrete inputs, native output, actual evidence and a review request. This pack has no publishing integration.\n\nNative action chain: Comment with hiring experience → Repost with a relevant lesson → Follow the evidence series → Visit the permissioned portfolio\n\nNative AI: LinkedIn’s March 2026 engineering account describes LLM/GPU-based ranking and professional-interest understanding. This supports clear career context, not a guaranteed recruiter result or knowledge of private weights.\nPrimary source: https://www.linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed\n\nReview actual account availability, documented inputs/outputs and a privacy/failure limit. Keep NOT VERIFIED when access is absent. Generate a native draft, not an invented performance report. Submit source, rights and preflight evidence before publication; log real outcomes only after authorized use.\n",
      "filename": "AGENT_OPERATING_MANUAL.md",
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      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn audience\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Map professional surfaces\nInventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.\n\nYour artifact / evidence:\n\n## Study two relevant career conversations\nRead two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.\n\nYour artifact / evidence:\n\n## Build the professional graph\nDraw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.\n\nYour artifact / evidence:\n\n## Write the nine identity layers\nDescribe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.\n\nYour artifact / evidence:\n\n## Redesign the profile as evidence\nDraft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.\n\nYour artifact / evidence:\n\n## Choose connected content pillars\nSelect three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.\n\nYour artifact / evidence:\n\n",
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    },
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      "resource_id": "R029",
      "name": "native-production.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn native-production\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Map professional surfaces\nInventory profile, company/showcase page, posts, articles, newsletters, comments, reactions, reposts, document posts, native video, Live, Groups, messages, skills and Featured. Choose a career post with peer feedback as the action.\n\nYour artifact / evidence:\n\n## Study two relevant career conversations\nRead two public interview/portfolio discussions and the official feed explanation. Record audience role, useful reply types, date and boundaries. Do not assert that every recruiter uses the same ranking system.\n\nYour artifact / evidence:\n\n## Build the professional graph\nDraw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.\n\nYour artifact / evidence:\n\n## Write the nine identity layers\nDescribe who you are, what you know, what you did, problems solved, people helped, evidence, convictions, lessons and desired associations. Mark fictional teaching fields as examples.\n\nYour artifact / evidence:\n\n## Redesign the profile as evidence\nDraft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.\n\nYour artifact / evidence:\n\n## Choose connected content pillars\nSelect three to five pillars and outline six sequential posts for interview preparation. Connect education, cases, hard lessons, research and frameworks to a recurring professional problem.\n\nYour artifact / evidence:\n\n## Write the professional semantic hook\nDraft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.\n\nYour artifact / evidence:\n\n## Build the ten-layer search stack\nMap speech, post, headline, About, experience, skills, comments, article/newsletter, portfolio and structured data. Choose natural job-role queries and identify public versus private surfaces.\n\nYour artifact / evidence:\n\n## Compare the controlled post pair\nUse studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.\n\nYour artifact / evidence:\n\n## Write a complete knowledge post\nUse question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.\n\nYour artifact / evidence:\n\n## Build the case-study object\nDocument who, problem, baseline, intervention, process, result evidence, failures and lesson. Select a real project if available or retain explicit example labels; never expose employer confidential material.\n\nYour artifact / evidence:\n\n## Plan original research and human signal\nDesign a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.\n\nYour artifact / evidence:\n\n## Create the ten-slide document\nScript problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.\n\nYour artifact / evidence:\n\n## Align native video and localization\nStoryboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.\n\nYour artifact / evidence:\n\n## Design articles, newsletters and replies\nOutline a long-form article, newsletter series and three peer replies. Route a substantive question through answer, discussion, FAQ, article and updated portfolio/course; treat comments as peer review.\n\nYour artifact / evidence:\n\n## Test recruiting and procurement clarity\nWrite a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.\n\nYour artifact / evidence:\n\n## Build an integrated knowledge footprint\nConnect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.\n\nYour artifact / evidence:\n\n## Design the ten-step and six-version capstone\nComplete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.\n\nYour artifact / evidence:\n\n## Plan ten experiments and a thirty-point review\nDefine entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.\n\nYour artifact / evidence:\n\n## Recompose and approve the career package\nCreate LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI.\n\nYour artifact / evidence:\n\n",
      "filename": "native-production.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R030",
      "name": "caption.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn caption\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Write the professional semantic hook\nDraft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.\n\nYour artifact / evidence:\n\n## Compare the controlled post pair\nUse studio.html to refine A/B for the same behavioral-interview topic and text format. Preserve an authentic voice while adding role, evidence and a specific peer question.\n\nYour artifact / evidence:\n\n## Write a complete knowledge post\nUse question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.\n\nYour artifact / evidence:\n\n## Create the ten-slide document\nScript problem, conventional-wisdom limit, model, framework, example, pitfalls, solution, checklist, sources and next action. Keep text selectable in a real PDF export; verify phone reading and avoid bitmap-only tiny text.\n\nYour artifact / evidence:\n\n## Align native video and localization\nStoryboard a 45-second project story with captions and matching post text. Prepare one translated cue or reviewer brief and keep job roles and artifact names stable.\n\nYour artifact / evidence:\n\n",
      "filename": "caption.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R031",
      "name": "diagnostic-log.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn diagnostic-log\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Redesign the profile as evidence\nDraft headline, About, one context/action/result experience item, Featured artifact and five relevant skills. Use unmeasured rather than invented percentages; check visibility before sharing.\n\nYour artifact / evidence:\n\n## Plan original research and human signal\nDesign a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.\n\nYour artifact / evidence:\n\n",
      "filename": "diagnostic-log.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R032",
      "name": "capstone.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn capstone\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Build an integrated knowledge footprint\nConnect personal graph, company knowledge, recruiting content, article, case portfolio and source ledger. Apply human, platform, retrieval and agent checks; explain why expertise should outlast feed reach.\n\nYour artifact / evidence:\n\n## Design the ten-step and six-version capstone\nComplete identity, ontology, experience ledger, portfolio, graph, pillars, six-part series, discoverability, companion action card and provenance. Save six maturity versions of the same project-evidence post.\n\nYour artifact / evidence:\n\n## Plan ten experiments and a thirty-point review\nDefine entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.\n\nYour artifact / evidence:\n\n## Recompose and approve the career package\nCreate LinkedIn post/document/newsletter/video and a canonical FAQ from one source. Describe a nightly B3 production debrief as an optional professional case. Separate current search/retrieval from speculative talent markets and SI.\n\nYour artifact / evidence:\n\n",
      "filename": "capstone.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R033",
      "name": "experiments.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# LinkedIn experiments\n\nTopic: Interview project evidence\nAudience: A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\n\nThe worksheet is a production draft. Save specific decisions, source URLs, dates and observed results. Never fill a result with imagined analytics.\n\n## Test recruiting and procurement clarity\nWrite a capability card: actual skills, evidence URLs, availability limits, supported environments and contact preference. Compare an employer hiring use case with B2B vendor evaluation. Do not automate outreach.\n\nYour artifact / evidence:\n\n## Plan ten experiments and a thirty-point review\nDefine entity, keyword, case, human signal, document, profile, series, case structure, localization and four-way tests. Expand audience, evidence, privacy, format, sources and future-claim checks into thirty concrete review questions.\n\nYour artifact / evidence:\n\n",
      "filename": "experiments.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R034",
      "name": "requirements-map.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# Shared-brief coverage\n\n### PART 1 — LinkedIn Fundamentals\nOwn exercise: Map professional surfaces\n\n### PART 2 — The LinkedIn Professional Graph\nOwn exercise: Build the professional graph\n\n### PART 3 — Building a Professional Identity: The B3 Professional Identity Stack\nOwn exercise: Write the nine identity layers\n\n### PART 4 — The LinkedIn Profile as a Structured Knowledge Object\nOwn exercise: Redesign the profile as evidence\n\n### PART 5 — LinkedIn Content Pillars: Building a Connected Body of Knowledge\nOwn exercise: Choose connected content pillars\n\n### PART 6 — LinkedIn Hooks & The Semantic Professional Hook\nOwn exercise: Write the professional semantic hook\n\n### PART 7 — LinkedIn SEO (Search Engine Optimization)\nOwn exercise: Build the ten-layer search stack\n\n### PART 8 — The B3 LinkedIn Keyword Stack (10 Layers)\nOwn exercise: Build the ten-layer search stack\n\n### PART 9 — LinkedIn Posts as Professional Knowledge: The B3 Knowledge Post\nOwn exercise: Write a complete knowledge post\n\n### PART 10 — Comments as Professional Knowledge: The LinkedIn Knowledge Loop\nOwn exercise: Design articles, newsletters and replies\n\n### PART 11 — Authentic Thought Leadership: The B3 Thought Leadership Formula\nOwn exercise: Plan original research and human signal\n\n### PART 12 — LinkedIn Newsletters and Long-Form Knowledge\nOwn exercise: Design articles, newsletters and replies\n\n### PART 13 — Multimodal LinkedIn: The B3 Multimodal Professional Content Stack\nOwn exercise: Align native video and localization\n\n### PART 14 — Document Posts (Carousels) as Structured Knowledge Packets\nOwn exercise: Create the ten-slide document\n\n### PART 15 — Case Studies as High-Value Knowledge: The B3 Case Study Object\nOwn exercise: Build the case-study object\n\n### PART 16 — Original Research & Empirical Benchmarking on LinkedIn\nOwn exercise: Plan original research and human signal\n\n### PART 17 — AI-Generated Content vs Human Signal\nOwn exercise: Plan original research and human signal\n\n### PART 18 — Multilingual Professional Knowledge\nOwn exercise: Align native video and localization\n\n### PART 19 — Entity-First LinkedIn\nOwn exercise: Build the ten-layer search stack\n\n### PART 20 — Professional Knowledge Graphs\nOwn exercise: Build the professional graph\n\n### PART 21 — RAG-Ready Professional Content: The B3 Professional Knowledge Object\nOwn exercise: Companion knowledge and truthful claims: mechanisms.md, claims.json, knowledge.json, rag.jsonl\n\n### PART 22 — Agent-Ready Professional Content\nOwn exercise: Test recruiting and procurement clarity\n\n### PART 23 — The AI Professional Discovery Test\nOwn exercise: Test recruiting and procurement clarity\n\n### PART 24 — The LinkedIn → AI Answer Pipeline\nOwn exercise: Run a source-linked retrieval probe; media_pack.py retrieve\n\n### PART 25 — The LinkedIn → AI Agent Workflow\nOwn exercise: Test recruiting and procurement clarity\n\n### PART 26 — Professional Provenance & Cryptographic Trust\nOwn exercise: Record provenance and a human approval decision; provenance-notes.md\n\n### PART 27 — LinkedIn as an Integrated Knowledge Base\nOwn exercise: Build an integrated knowledge footprint\n\n### PART 28 — From Personal Brand to Personal Knowledge Graph\nOwn exercise: Build an integrated knowledge footprint\n\n### PART 29 — The B3 Professional Content Stack (10 Levels)\nOwn exercise: Design the ten-step and six-version capstone\n\n### PART 30 — The B3 Professional AI Readiness Score (0–100)\nOwn exercise: Use the readiness calculator; studio.html and rubric.json\n\n### PART 31 — LinkedIn for Enterprise & Corporate Knowledge\nOwn exercise: Build an integrated knowledge footprint\n\n### PART 32 — LinkedIn for B2B Social Selling & Agentic Procurement\nOwn exercise: Test recruiting and procurement clarity\n\n### PART 33 — LinkedIn for Technical Recruiting & Agentic Talent Matching\nOwn exercise: Test recruiting and procurement clarity\n\n### PART 34 — The LinkedIn Content Factory\nOwn exercise: Recompose and approve the career package\n\n### PART 35 — Late Night with Build Bots Better Connection\nOwn exercise: Recompose and approve the career package\n\n### PART 36 — The B3 Professional Knowledge Flywheel\nOwn exercise: Design articles, newsletters and replies\n\n### PART 37 — The B3 Four-Way Optimization Model\nOwn exercise: Build an integrated knowledge footprint\n\n### PART 38 — Beyond the LinkedIn Algorithm\nOwn exercise: Build an integrated knowledge footprint\n\n### PART 39 — The Future Professional: Creator + Engineer + Agent Orchestrator\nOwn exercise: Recompose and approve the career package\n\n### PART 40 — The B3 10 Empirical Experiments\nOwn exercise: Plan ten experiments and a thirty-point review\n\n### PART 41 — The B3 LinkedIn Content Audit Checklist\nOwn exercise: Plan ten experiments and a thirty-point review\n\n### PART 42 — The Future of LinkedIn & Professional AI\nOwn exercise: Recompose and approve the career package\n\n### PART 43 — The Evolution of Professional Discovery\nOwn exercise: Recompose and approve the career package\n\n",
      "filename": "requirements-map.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R035",
      "name": "strategy.json",
      "type": "json",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "{\n  \"platform\": \"LinkedIn\",\n  \"progression\": [\n    \"Professional content\",\n    \"LinkedIn optimization\",\n    \"Professional search\",\n    \"AI discoverability\",\n    \"Retrieval\",\n    \"Agentic discovery\",\n    \"Future intelligence (speculative)\"\n  ],\n  \"epistemic_status\": \"Editorial framework; optimization stages do not guarantee distribution or learning\",\n  \"audiences\": [\n    \"human\",\n    \"platform\",\n    \"search/retrieval\",\n    \"authorized agent\"\n  ],\n  \"human_utility\": \"A professional preparing for interviews wants credible career evidence and useful peer feedback. The example teaches how to explain one project outcome without inventing experience or performance figures.\",\n  \"measurement\": \"Compare the same topic, format, audience and promise. Change one independent variable per audience test.\"\n}\n",
      "filename": "strategy.json",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R036",
      "name": "strategy.json.VERSION_HISTORY.txt",
      "type": "txt",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n",
      "filename": "strategy.json.VERSION_HISTORY.txt",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    },
    {
      "resource_id": "R037",
      "name": "mechanisms.md",
      "type": "md",
      "purpose": "Working resource used by this Build; inspect before running.",
      "contents": "VERSION HISTORY\n2026-10-06 EDT | chatgpt_codex | media-catchup | v1: original learner production resource.\n\n# Discovery mechanisms and truth labels\n\nTraining and fine-tuning alter model weights; publishing a post cannot force either. RLHF/RLAIF uses preference data and is separate from inference. Crawling and public/in-app search build accessible indexes; private posts, robots rules, terms and APIs constrain access. Embeddings represent semantic similarity; vector databases retrieve vectors. RAG retrieves external chunks into context, while context windows are temporary inference inputs. Graphs encode named relationships. Recommendation systems rank candidate content for an audience. AI answers and citations may summarize accessible sources; citations are observed, not guaranteed. Provenance records origin and byte changes. Machine-readable schemas describe content but do not force rich results. Multimodal OCR/ASR interprets image/audio and requires human correction. Agent browsing/retrieval adds goal-driven tool actions. Licensed and enterprise repositories have explicit access contracts. Platform-specific AI features must be checked for account/region/version availability. Future SI learning and coordination is speculative.\n\nTaxonomy: ESTABLISHED requires a direct source; EVIDENCE-SUPPORTED requires reproducible observations; EMERGING describes a dated deployment; HYPOTHESIS is a proposed test; SPECULATION is a future possibility. The shared briefs use different counts/groupings (13,16,18); retain distinctions rather than inventing a uniform number. SI is not a promise of permanent model learning or a synonym for semantic indexing.\n",
      "filename": "mechanisms.md",
      "generated_from": "BUILD_SPEC.json.resources",
      "version": "2.0"
    }
  ],
  "bot_coach": {
    "bot_name": "Buzz",
    "bot_role": "Offline reviewed step coach",
    "personality": "patient and concrete",
    "tone": "plain",
    "teaching_style": "hint, diagnostic, action, checkpoint",
    "introduction": "Ask about the current step. I use reviewed local answers and queue unknown questions for review.",
    "encouragement_style": "Focus on observed results.",
    "troubleshooting_style": "Ask for the smallest reproducible evidence.",
    "limits": "This offline coach does not call an LLM or inspect your computer. Unknown questions enter a local exportable review queue, not a shared SQL FAQ."
  },
  "coach_interaction_model": {
    "stuck": "current diagnostic and first repair",
    "meaning": "current explanation",
    "error": "current troubleshooting",
    "why": "purpose",
    "skip": "require equivalent evidence",
    "alternative": "documented alternative",
    "finished": "record actual output",
    "start_over": "export then reset only this Build",
    "progressive_assistance": [
      "hint",
      "diagnostic",
      "walkthrough",
      "resource solution"
    ]
  },
  "website_specification": {
    "layout": "Lesson outline, actionable step, current-context coach",
    "features": [
      "platform selectors",
      "command copy with selection fallback",
      "evidence checklist",
      "practical quiz",
      "downloadable resource ZIP",
      "local progress",
      "evidence export",
      "unknown-question review queue"
    ],
    "checkpoint_model": "Learner supplies observations/output; tokens check expected shape only, not host truth.",
    "navigation": "Browse freely; checked next-button progression retains evidence.",
    "video": "spec-driven walkthrough and optional speech; full rendered media is a downstream production task."
  },
  "final_test": {
    "test_name": "Repeat the core workflow and restore its evidence",
    "instructions": "Produce your native professional text post and its same-topic comparison, complete the detailed capstone plan, then review source facts, rights, current community rules and actual media. Run the meaningful failure/repair and source-linked retrieval checks. Export course evidence. A structural pack pass is separate from live publication, audience results or independent quality approval.",
    "required_inputs": [
      "workspace_guard.py",
      "restore_drill.py",
      "onboarding-checklist.md",
      "media_pack.py",
      "versions.json",
      "knowledge.json",
      "agent-card.json",
      "rubric.json",
      "native.json",
      "claims.json",
      "metrics.json",
      "provenance-notes.md",
      "agent-training.json",
      "AGENT_OPERATING_MANUAL.md",
      "audience.md",
      "native-production.md",
      "caption.md",
      "diagnostic-log.md",
      "capstone.md",
      "experiments.md",
      "requirements-map.md",
      "strategy.json",
      "mechanisms.md"
    ],
    "success_conditions": [
      "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
      "Core output can be reproduced",
      "Guard limit explained",
      "Backup and restoration evidence retained"
    ],
    "automated_tests": "catalog_check.py records browser behavior and executable resources, with explicit scope.",
    "manual_tests": [
      "Review actual account sign-in where needed",
      "Check source/data assumptions",
      "Restore one real non-secret memory file"
    ],
    "expected_output": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "failure_paths": "Use the specific step diagnostic, retain failure evidence and stop when access is required."
  },
  "extensions": [
    {
      "title": "Use your own input",
      "action": "Replace the sample with one permitted real input and rerun the same checks."
    },
    {
      "title": "Add one failure case",
      "action": "Create a malformed or missing input; prove the workflow fails visibly and recovers."
    },
    {
      "title": "Automate after review",
      "action": "Write a bounded schedule with logging and a stop condition; obtain approval for external actions separately."
    }
  ],
  "metadata": {
    "SEO_title": "Creating LinkedIn Content for the AI, SI, and Agentic Era | BuildBotsBetter",
    "SEO_description": "A reviewed professional text post for Interview project evidence, controlled A/B drafts, complete native production/capstone plans, an evidence rubric, source-linked retrieval objects and hash-checked companion resources.",
    "SEO_keywords": [
      "linkedin-content-ai-si-agentic-era",
      "guided build",
      "verified outcome"
    ],
    "difficulty": "beginner",
    "related_builds": [
      "dogfooding",
      "buildbotsbetter-onboarding"
    ],
    "next_recommended_build": "dogfooding"
  },
  "automation_map": [
    {
      "output": "website",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "walkthrough",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "bot dialogue",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "resources",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "quiz",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "captions",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    },
    {
      "output": "metadata",
      "generated_from": "BUILD_SPEC.json",
      "human_review_required": true
    }
  ],
  "quality_assurance_report": {
    "scope": "Original instructional content, reviewed against shared brief; execution and browser receipts are stored under logs/catalog-acceptance.",
    "known_limits": [
      "Platform access, account features and upload limits require current account checks.",
      "No content was published and no genuine audience metrics or AI citations are claimed.",
      "Studio and lint check draft structure; factual accuracy, media quality, OCR/ASR and community fit need human review.",
      "The provided fixture is synthetic. Actual content creation and live feedback must not be inferred from a browser walkthrough."
    ]
  },
  "sources": [
    {
      "title": "Shared LinkedIn requirements",
      "url": "../../specs/linkedin-content-ai-si-agentic-era.md"
    },
    {
      "title": "LinkedIn primary platform guidance",
      "url": "https://www.linkedin.com/help/linkedin/answer/a1339724"
    },
    {
      "title": "LinkedIn documented native AI capabilities",
      "url": "https://www.linkedin.com/blog/engineering/feed/engineering-the-next-generation-of-linkedins-feed"
    }
  ],
  "known_uncertainties": [
    "Platform access, account features and upload limits require current account checks.",
    "No content was published and no genuine audience metrics or AI citations are claimed.",
    "Studio and lint check draft structure; factual accuracy, media quality, OCR/ASR and community fit need human review.",
    "The provided fixture is synthetic. Actual content creation and live feedback must not be inferred from a browser walkthrough."
  ],
  "requirement_coverage": {
    "shared_parts": [
      "### PART 1 — LinkedIn Fundamentals",
      "### PART 2 — The LinkedIn Professional Graph",
      "### PART 3 — Building a Professional Identity: The B3 Professional Identity Stack",
      "### PART 4 — The LinkedIn Profile as a Structured Knowledge Object",
      "### PART 5 — LinkedIn Content Pillars: Building a Connected Body of Knowledge",
      "### PART 6 — LinkedIn Hooks & The Semantic Professional Hook",
      "### PART 7 — LinkedIn SEO (Search Engine Optimization)",
      "### PART 8 — The B3 LinkedIn Keyword Stack (10 Layers)",
      "### PART 9 — LinkedIn Posts as Professional Knowledge: The B3 Knowledge Post",
      "### PART 10 — Comments as Professional Knowledge: The LinkedIn Knowledge Loop",
      "### PART 11 — Authentic Thought Leadership: The B3 Thought Leadership Formula",
      "### PART 12 — LinkedIn Newsletters and Long-Form Knowledge",
      "### PART 13 — Multimodal LinkedIn: The B3 Multimodal Professional Content Stack",
      "### PART 14 — Document Posts (Carousels) as Structured Knowledge Packets",
      "### PART 15 — Case Studies as High-Value Knowledge: The B3 Case Study Object",
      "### PART 16 — Original Research & Empirical Benchmarking on LinkedIn",
      "### PART 17 — AI-Generated Content vs Human Signal",
      "### PART 18 — Multilingual Professional Knowledge",
      "### PART 19 — Entity-First LinkedIn",
      "### PART 20 — Professional Knowledge Graphs",
      "### PART 21 — RAG-Ready Professional Content: The B3 Professional Knowledge Object",
      "### PART 22 — Agent-Ready Professional Content",
      "### PART 23 — The AI Professional Discovery Test",
      "### PART 24 — The LinkedIn → AI Answer Pipeline",
      "### PART 25 — The LinkedIn → AI Agent Workflow",
      "### PART 26 — Professional Provenance & Cryptographic Trust",
      "### PART 27 — LinkedIn as an Integrated Knowledge Base",
      "### PART 28 — From Personal Brand to Personal Knowledge Graph",
      "### PART 29 — The B3 Professional Content Stack (10 Levels)",
      "### PART 30 — The B3 Professional AI Readiness Score (0–100)",
      "### PART 31 — LinkedIn for Enterprise & Corporate Knowledge",
      "### PART 32 — LinkedIn for B2B Social Selling & Agentic Procurement",
      "### PART 33 — LinkedIn for Technical Recruiting & Agentic Talent Matching",
      "### PART 34 — The LinkedIn Content Factory",
      "### PART 35 — Late Night with Build Bots Better Connection",
      "### PART 36 — The B3 Professional Knowledge Flywheel",
      "### PART 37 — The B3 Four-Way Optimization Model",
      "### PART 38 — Beyond the LinkedIn Algorithm",
      "### PART 39 — The Future Professional: Creator + Engineer + Agent Orchestrator",
      "### PART 40 — The B3 10 Empirical Experiments",
      "### PART 41 — The B3 LinkedIn Content Audit Checklist",
      "### PART 42 — The Future of LinkedIn & Professional AI",
      "### PART 43 — The Evolution of Professional Discovery"
    ],
    "part_to_exercise": [
      {
        "requirement": "### PART 1 — LinkedIn Fundamentals",
        "exercise": "Map professional surfaces"
      },
      {
        "requirement": "### PART 2 — The LinkedIn Professional Graph",
        "exercise": "Build the professional graph"
      },
      {
        "requirement": "### PART 3 — Building a Professional Identity: The B3 Professional Identity Stack",
        "exercise": "Write the nine identity layers"
      },
      {
        "requirement": "### PART 4 — The LinkedIn Profile as a Structured Knowledge Object",
        "exercise": "Redesign the profile as evidence"
      },
      {
        "requirement": "### PART 5 — LinkedIn Content Pillars: Building a Connected Body of Knowledge",
        "exercise": "Choose connected content pillars"
      },
      {
        "requirement": "### PART 6 — LinkedIn Hooks & The Semantic Professional Hook",
        "exercise": "Write the professional semantic hook"
      },
      {
        "requirement": "### PART 7 — LinkedIn SEO (Search Engine Optimization)",
        "exercise": "Build the ten-layer search stack"
      },
      {
        "requirement": "### PART 8 — The B3 LinkedIn Keyword Stack (10 Layers)",
        "exercise": "Build the ten-layer search stack"
      },
      {
        "requirement": "### PART 9 — LinkedIn Posts as Professional Knowledge: The B3 Knowledge Post",
        "exercise": "Write a complete knowledge post"
      },
      {
        "requirement": "### PART 10 — Comments as Professional Knowledge: The LinkedIn Knowledge Loop",
        "exercise": "Design articles, newsletters and replies"
      },
      {
        "requirement": "### PART 11 — Authentic Thought Leadership: The B3 Thought Leadership Formula",
        "exercise": "Plan original research and human signal"
      },
      {
        "requirement": "### PART 12 — LinkedIn Newsletters and Long-Form Knowledge",
        "exercise": "Design articles, newsletters and replies"
      },
      {
        "requirement": "### PART 13 — Multimodal LinkedIn: The B3 Multimodal Professional Content Stack",
        "exercise": "Align native video and localization"
      },
      {
        "requirement": "### PART 14 — Document Posts (Carousels) as Structured Knowledge Packets",
        "exercise": "Create the ten-slide document"
      },
      {
        "requirement": "### PART 15 — Case Studies as High-Value Knowledge: The B3 Case Study Object",
        "exercise": "Build the case-study object"
      },
      {
        "requirement": "### PART 16 — Original Research & Empirical Benchmarking on LinkedIn",
        "exercise": "Plan original research and human signal"
      },
      {
        "requirement": "### PART 17 — AI-Generated Content vs Human Signal",
        "exercise": "Plan original research and human signal"
      },
      {
        "requirement": "### PART 18 — Multilingual Professional Knowledge",
        "exercise": "Align native video and localization"
      },
      {
        "requirement": "### PART 19 — Entity-First LinkedIn",
        "exercise": "Build the ten-layer search stack"
      },
      {
        "requirement": "### PART 20 — Professional Knowledge Graphs",
        "exercise": "Build the professional graph"
      },
      {
        "requirement": "### PART 21 — RAG-Ready Professional Content: The B3 Professional Knowledge Object",
        "exercise": "Companion knowledge and truthful claims: mechanisms.md, claims.json, knowledge.json, rag.jsonl"
      },
      {
        "requirement": "### PART 22 — Agent-Ready Professional Content",
        "exercise": "Test recruiting and procurement clarity"
      },
      {
        "requirement": "### PART 23 — The AI Professional Discovery Test",
        "exercise": "Test recruiting and procurement clarity"
      },
      {
        "requirement": "### PART 24 — The LinkedIn → AI Answer Pipeline",
        "exercise": "Run a source-linked retrieval probe; media_pack.py retrieve"
      },
      {
        "requirement": "### PART 25 — The LinkedIn → AI Agent Workflow",
        "exercise": "Test recruiting and procurement clarity"
      },
      {
        "requirement": "### PART 26 — Professional Provenance & Cryptographic Trust",
        "exercise": "Record provenance and a human approval decision; provenance-notes.md"
      },
      {
        "requirement": "### PART 27 — LinkedIn as an Integrated Knowledge Base",
        "exercise": "Build an integrated knowledge footprint"
      },
      {
        "requirement": "### PART 28 — From Personal Brand to Personal Knowledge Graph",
        "exercise": "Build an integrated knowledge footprint"
      },
      {
        "requirement": "### PART 29 — The B3 Professional Content Stack (10 Levels)",
        "exercise": "Design the ten-step and six-version capstone"
      },
      {
        "requirement": "### PART 30 — The B3 Professional AI Readiness Score (0–100)",
        "exercise": "Use the readiness calculator; studio.html and rubric.json"
      },
      {
        "requirement": "### PART 31 — LinkedIn for Enterprise & Corporate Knowledge",
        "exercise": "Build an integrated knowledge footprint"
      },
      {
        "requirement": "### PART 32 — LinkedIn for B2B Social Selling & Agentic Procurement",
        "exercise": "Test recruiting and procurement clarity"
      },
      {
        "requirement": "### PART 33 — LinkedIn for Technical Recruiting & Agentic Talent Matching",
        "exercise": "Test recruiting and procurement clarity"
      },
      {
        "requirement": "### PART 34 — The LinkedIn Content Factory",
        "exercise": "Recompose and approve the career package"
      },
      {
        "requirement": "### PART 35 — Late Night with Build Bots Better Connection",
        "exercise": "Recompose and approve the career package"
      },
      {
        "requirement": "### PART 36 — The B3 Professional Knowledge Flywheel",
        "exercise": "Design articles, newsletters and replies"
      },
      {
        "requirement": "### PART 37 — The B3 Four-Way Optimization Model",
        "exercise": "Build an integrated knowledge footprint"
      },
      {
        "requirement": "### PART 38 — Beyond the LinkedIn Algorithm",
        "exercise": "Build an integrated knowledge footprint"
      },
      {
        "requirement": "### PART 39 — The Future Professional: Creator + Engineer + Agent Orchestrator",
        "exercise": "Recompose and approve the career package"
      },
      {
        "requirement": "### PART 40 — The B3 10 Empirical Experiments",
        "exercise": "Plan ten experiments and a thirty-point review"
      },
      {
        "requirement": "### PART 41 — The B3 LinkedIn Content Audit Checklist",
        "exercise": "Plan ten experiments and a thirty-point review"
      },
      {
        "requirement": "### PART 42 — The Future of LinkedIn & Professional AI",
        "exercise": "Recompose and approve the career package"
      },
      {
        "requirement": "### PART 43 — The Evolution of Professional Discovery",
        "exercise": "Recompose and approve the career package"
      }
    ],
    "native_exercises": [
      "Map professional surfaces",
      "Study two relevant career conversations",
      "Build the professional graph",
      "Write the nine identity layers",
      "Redesign the profile as evidence",
      "Choose connected content pillars",
      "Write the professional semantic hook",
      "Build the ten-layer search stack",
      "Compare the controlled post pair",
      "Write a complete knowledge post",
      "Build the case-study object",
      "Plan original research and human signal",
      "Create the ten-slide document",
      "Align native video and localization",
      "Design articles, newsletters and replies",
      "Test recruiting and procurement clarity",
      "Build an integrated knowledge footprint",
      "Design the ten-step and six-version capstone",
      "Plan ten experiments and a thirty-point review",
      "Recompose and approve the career package"
    ],
    "operational_safeguards": "Last lesson; resolved-path dry run, active tool guard check, scheduled off-volume backup and restore proof.",
    "time_allocation": "Native production exercises: 250 minutes; preparation: 6 minutes; knowledge pack: 24 minutes; operational safeguards: 30 minutes. Native work is 250/310 = 80.6% of planned instructional time."
  }
}