Dogfooding: Use What You Build

# audit_checklist.py - 6-dimension adversarial audit rubric AUDIT_DIMENSIONS = [ "design_and_layout", # Visual polish, typography, responsive mobile wrap "content_quality", # Technical accuracy, no banned words or dummy values "technical_accuracy", # Multi-platform commands execute without errors "interactivity_and_coach", # Dynamic checkpoints, boundary buttons, smart coach "pedagogy_and_flow", # Natural step ordering, clear success criteria "production_readiness" # Background daemons, schedulers, Run It Later cards ] def print_audit_questions(): print("=== ADVERSARIAL AUDIT CHECKLIST ===") print("1. Did you assume flaws exist before looking?") print("2. Are disabled buttons clearly explained?") print("3. Can any unnecessary clicks or reading be eliminated?") print("4. Does the Bot Coach answer contextually or give repetitive replies?") print("5. Are failure modes and error codes anticipated?") return True print_audit_questions()

# dogfood_walk.py - Playwright automated walk from playwright.sync_api import sync_playwright def dogfood_run(html_url: str): with sync_playwright() as p: browser = p.chromium.launch(headless=True) page = browser.new_page() console_errors = [] page.on("console", lambda msg: console_errors.append(msg.text) if msg.type == "error" else None) page.on("pageerror", lambda err: console_errors.append(err.message)) page.goto(html_url) page.wait_for_load_state("domcontentloaded") # Test navigation steps = page.locator(".step-nav-item").all() print(f"Discovered {len(steps)} guided steps.") print("Asserting zero console errors...") assert len(console_errors) == 0, f"Errors found: {console_errors}" print("Walk completed successfully with 0 console errors.") browser.close() print("Dogfood walk engine compiled successfully.")

# models.py - Structured dogfood issue model from pydantic import BaseModel, Field from enum import Enum class SeverityTier(str, Enum): BLOCKER = "BLOCKER" MAJOR = "MAJOR" MINOR = "MINOR" OPPORTUNITY = "OPPORTUNITY" NIT = "NIT" class DogfoodIssue(BaseModel): issue_code: str dimension: str severity: SeverityTier step_id: str description: str reproduction_steps: list[str] suggested_fix: str issue = DogfoodIssue( issue_code="DOG-001", dimension="pedagogy_and_flow", severity=SeverityTier.OPPORTUNITY, step_id="step-2", description="Command requires manual city editing without a helper prompt.", reproduction_steps=["Open Step 2", "Attempt to copy URL"], suggested_fix="Add regional URL generator input in DOM." ) print("Validated DogfoodIssue:", issue.model_dump())

# telemetry.py - Format dogfood findings into self-audit review payload import json, subprocess, sys def get_current_release_tag() -> str: try: proc = subprocess.run(["git", "describe", "--tags", "--abbrev=0"], capture_output=True, text=True) if proc.returncode == 0 and proc.stdout.strip(): return proc.stdout.strip() except Exception: pass return "current-release" def generate_self_audit_payload(cycle_id: int, tag: str, issues: list) -> str: comments = [] for idx, iss in enumerate(issues, 1): comments.append({ "code": f"agy-{tag}-{idx:03d}", "severity": iss["severity"], "category": "UX", "build": "dogfooding", "body": iss["description"], "suggestion": iss["suggested_fix"], "technique": "T20" }) payload = { "cycle_id": cycle_id, "round": 1, "owner": "antigravity_cli", "tag": tag, "summary": "Dogfooding automated telemetry audit.", "comments": comments } return json.dumps(payload, indent=2) current_tag = get_current_release_tag() print("Generated Review Payload Sample:") print(generate_self_audit_payload(2, current_tag, [ {"severity": "OPPORTUNITY", "description": "Add city picker", "suggested_fix": "Add selector"} ]))

# sweep_all.py - Daily automated regression sweep import subprocess import glob def run_regression_sweep(): test_files = glob.glob("test_*.py") if not test_files: raise SystemExit("ERROR: 0 automated test suites discovered. Cannot sweep empty directory.") print(f"Discovered {len(test_files)} automated test suites.") passed = 0 for tf in test_files: print(f"Running dogfood suite: {tf}...") res = subprocess.run(["python", tf], capture_output=True, text=True) if res.returncode == 0: passed += 1 print(f" [PASS] {tf}") else: print(f" [FAIL] {tf}") print(f"Sweep completed: {passed}/{len(test_files)} passed. Zero regressions detected.") run_regression_sweep()