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Get Started Free →The method for finding the gap between what a system is supposed to do and what the code actually does — the class of bug generic scanners miss because they have no model of intent. Defines what counts as documented intent, what counts as implementation evidence, which mismatches matter, and how to avoid hand-wavy findings. Use when auditing AI-built code, reviewing access control against documented permissions, or checking whether a codebase matches its own documentation.
| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 568% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -19% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 62% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -9% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 18% | 0% |
A linter scans code in a vacuum. It can tell you the code is internally consistent; it cannot tell you the code does what you meant, because it has no model of your intent. The highest-value security and correctness bugs live in that gap — a permission documented but never enforced, a "cron-only" endpoint anyone can call, a field marked public-only that leaks private data.
This skill is the method for finding that gap. It is the differentiator: it only works when intent has been written down first (see the shipping-artifacts skill), and that's exactly why commodity tools can't replicate it.
Use this when documented intent exists — permissions.md, architecture.md, variables.md, etc. If those docs are absent or stale, that absence is itself the first finding: you cannot audit intent you never recorded. Recommend documenting first, then auditing.
documentation/*.md set as the source of truth for what should be true: who may access what, which boundaries are trusted, which data is public. Treat the docs as claims to verify, not as proof.Other measured skills in the registry, with their headline benchmark lift.