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.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionintended-vs-implementedExecute the skills CLI command in your project's root directory to begin installation:
Fetches intended-vs-implemented from phuryn/pm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate intended-vs-implemented. Access via /intended-vs-implemented in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
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| name | intended-vs-implemented |
| description | "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." |
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.
Establish intent. Read the /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.
Gather implementation evidence. Read the code that enforces (or fails to enforce) each claim. Evidence is a cited file and line — the actual authorization check, the actual query filter, the actual sanitizer. "It's probably handled upstream" is not evidence; the code path is.
Compare claim to code, one boundary at a time. For each documented rule, ask: does an enforcement point actually implement it, on the server, on every path? Distrust comments like "internal only," "admin only," or "validated elsewhere" — verify them in code.
Classify each mismatch by whether it matters. A mismatch matters when crossing it lets a real actor reach data, money, infrastructure, or another tenant they shouldn't. It does not matter when the only person affected is the actor themselves on their own data. Drop cosmetic drift; keep boundary-crossing drift.
Avoid hand-wavy findings. Every finding names: the documented intent (quote the doc), the implemented reality (cite the code), the attacker and victim, and the concrete fix. If you cannot cite both sides of the gap, it is a question to investigate, not a finding to report.
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
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We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend intended-vs-implemented for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
intended-vs-implemented reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: intended-vs-implemented is the kind of skill you can hand to a new teammate without a long onboarding doc.
intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: intended-vs-implemented is the kind of skill you can hand to a new teammate without a long onboarding doc.
intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
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