Creating LinkedIn Content for the AI, SI, and Agentic Era

Open the native draft studio

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 No account or terminal is needed for the studio; do not publish a synthetic fixture.

Expected: The studio shows A/B drafts and a zero-initialized rubric; the content-agent plan has twenty operating points and native CTA/AI boundaries.

Map professional surfaces

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.

Expected: A professional surface map and clear audience action exist.

Study two relevant career conversations

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.

Expected: Two observations support a professional persona and tone.

Build the professional graph

Draw person → skill → role, person → company → industry and skill → required for → role. Add the actual portfolio artifacts that support each claim.

Expected: A graph connects identity, role, expertise and evidence.

Write the nine identity layers

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.

Expected: Nine identity fields contain no invented personal achievements.

Redesign the profile as evidence

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.

Expected: Five profile surfaces align with a verified project.

Choose connected content pillars

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.

Expected: Pillars and a six-part series form a connected body of knowledge.

Write the professional semantic hook

Draft topic + audience + problem + tangible value. Compare vague confidence advice with a specific project-evidence framework without pretending to have client results.

Expected: Two hooks show a concrete professional benefit.

Build the ten-layer search stack

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.

Expected: Ten consistent layers address one professional intent.

Compare the controlled post pair

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.

Expected: A/B drafts retain the same audience and interview problem.

Write a complete knowledge post

Use question → context → insight → evidence → example → lesson → action. Add a direct source and identify any example case as synthetic rather than a client testimonial.

Expected: A complete post has a checkable evidence boundary.

Build the case-study object

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.

Expected: Eight case fields identify measured and unmeasured outcomes.

Plan original research and human signal

Design a small interview-feedback study with participant consent, questions and reporting limits. Explain experience + insight + evidence + originality + consistency without fake surveys or endorsements.

Expected: A research protocol and honest originality statement exist.

Create the ten-slide document

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.

Expected: Ten career-focused slide scripts have clear evidence and sources.

Align native video and localization

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.

Expected: Speech, slide, caption and localized cue preserve the same claims.

Design articles, newsletters and replies

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.

Expected: A short-to-long plan and a feedback loop preserve corrections.

Test recruiting and procurement clarity

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.

Expected: Two evaluator personas can verify capabilities from evidence.

Build an integrated knowledge footprint

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.

Expected: A four-audience review connects long-lived professional artifacts.

Design the ten-step and six-version capstone

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.

Expected: Ten portfolio components and six additive versions exist.

Plan ten experiments and a thirty-point review

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.

Expected: Ten proposals and thirty preflight questions have owner and evidence.

Recompose and approve the career package

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.

Expected: A professional approval record names unverified live outcomes.

Set up the practice folder

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.

Expected: Python reports version 3.10 or newer and the terminal is in the extracted resource folder.

windows

python --version
Get-Location
Get-ChildItem

macos

python3 --version
pwd
ls

linux

python3 --version
pwd
ls

Audit the mechanism and claim ledger

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.

Expected: The ledger separates training, retrieval, indexing, context, citation and action; two source-linked chunks stand alone.

Use the readiness calculator

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.

Expected: A downloadable pair and rubric are saved; the score is self-assessment and unrun metrics are explicit.

Hash and lint the reviewed files

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.

Expected: HASH PASS and MEDIA PASS appear with exit 0; the readiness score matches the reviewed rubric.

windows

python media_pack.py hash
python media_pack.py lint
python media_pack.py score

macos

python3 media_pack.py hash
python3 media_pack.py lint
python3 media_pack.py score

linux

python3 media_pack.py hash
python3 media_pack.py lint
python3 media_pack.py score

Prove changed bytes and broken content fail

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.

Expected: A mismatched topic produces MEDIA FAIL; a reviewed correction produces MEDIA PASS.

windows

python media_pack.py lint
# After correcting and reviewing the topic:
python media_pack.py hash
python media_pack.py lint

macos

python3 media_pack.py lint
# After correcting and reviewing the topic:
python3 media_pack.py hash
python3 media_pack.py lint

linux

python3 media_pack.py lint
# After correcting and reviewing the topic:
python3 media_pack.py hash
python3 media_pack.py lint

Run a source-linked retrieval probe

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.

Expected: The relevant query returns source-linked chunks; the unrelated query abstains.

windows

python media_pack.py retrieve --query "What makes project evidence useful in a behavioral interview?"
python media_pack.py retrieve --query "quasar waffle weather"

macos

python3 media_pack.py retrieve --query "What makes project evidence useful in a behavioral interview?"
python3 media_pack.py retrieve --query "quasar waffle weather"

linux

python3 media_pack.py retrieve --query "What makes project evidence useful in a behavioral interview?"
python3 media_pack.py retrieve --query "quasar waffle weather"

Record provenance and a human approval decision

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.

Expected: The handoff includes native drafts, source/rights review, unsigned integrity manifest, explicit live-status limits and an approval decision.

Write a repeatable onboarding checklist

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.

Expected: The checklist contains a concrete path and owner for rules, memory, permissions, guard, backup, restore and final verification.

Demonstrate the variable-path limit safely

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.

Expected: GUARD PASS reports inside allowed and profile, outside, variable-derived profile and workspace root denied; no deletion was executed.

windows

python workspace_guard.py --self-test

macos

python3 workspace_guard.py --self-test

linux

python3 workspace_guard.py --self-test

Verify the actual protection in your tool

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.

Expected: 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.

Enable a real backup of memory and project files

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.

Expected: A backup on another drive includes the tool’s user settings and memory folder and this project, and a successful schedule/time is recorded.

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

Practice a hash-verified restore

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.

Expected: RESTORE PASS contains matching hashes; the actual memory file is readable after restoration into scratch.

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