Catches AI-specific half-finished code patterns that slip into production: mock API responses in real handlers, hardcoded test credentials in fallbacks, stub returns, debug bypasses, and dev-only routes left active.
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PATCH — telemetry-format prose fix: verification instructions still required dotted check IDs (audit.{category}.{check}) while the JSON template and the ingest schema use bare kebab-case slugs; an AI following the prose over the template would emit IDs the schema rejects with a 400. Prose now states bare slugs. No check or criteria changes.
Picked by pack overlap with this audit.
Catches LLM hallucination — code that references modules, files, routes, schemas, env vars, or assets that do not exist anywhere in the project.
Catches code that will surprise-bill on the first viral moment — non-LLM cost vectors that AI tools commonly leave unbounded: file uploads with no size limit, unbounded DB queries, email/SMS without rate limits, webhooks without idempotency, and background jobs without retry caps.
Catches multi-session AI confusion: codebases that accumulate multiple libraries doing the same job because the model picked differently across sessions, resulting in dependency cruft and split-brain data layers.
Copy the prompt in your preferred format above.
Paste into your AI coding tool (Claude Code, Cursor, Bolt, etc.).
Let the AI run all checks. Review the structured JSON output it produces.
Submit the JSON telemetry block to AuditBuffet for scoring and benchmarks.
Paste your JSON telemetry to get scores and benchmarks.
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