intended-vs-implemented

phuryn/pm-skills · updated Jun 11, 2026

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$npx skills install phuryn/pm-skills/intended-vs-implemented
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summary

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.

skill.md
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."

Intended vs. Implemented: Auditing the Gap

Purpose

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.

Context

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.

Method

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

What counts

  • Intent: a documented rule, boundary, scope, or public/private classification.
  • Implementation evidence: a cited enforcement point (or its provable absence) in the code.
  • A mismatch that matters: doc says one thing, code does another, and the difference crosses a trust, cost, data, or tenant boundary.

Notes

  • Documented-but-unenforced is a finding on its own — rank it by what crossing the gap exposes.
  • Undocumented-but-enforced is usually fine, but flag it: the docs are now stale, which weakens the next audit.
  • This method feeds the security and performance audits; it does not replace their sink-level analysis — it adds the intent axis they lack.
  • Never fabricate intent to manufacture a gap. If the docs are silent, say the docs are silent.
how to use intended-vs-implemented

How to use intended-vs-implemented on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add intended-vs-implemented
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills install phuryn/pm-skills/intended-vs-implemented

The skills CLI fetches intended-vs-implemented from GitHub repository phuryn/pm-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/intended-vs-implemented

Reload or restart Cursor to activate intended-vs-implemented. Access the skill through slash commands (e.g., /intended-vs-implemented) or your agent's skill management interface.

Security & Verification Notice

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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.457 reviews
  • Piyush G· Dec 28, 2024

    We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Min Anderson· Dec 24, 2024

    I recommend intended-vs-implemented for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kiara Khan· Dec 24, 2024

    We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • James Ghosh· Dec 16, 2024

    intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Anaya Thompson· Dec 16, 2024

    intended-vs-implemented reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Nikhil Verma· Dec 12, 2024

    Keeps context tight: intended-vs-implemented is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Shikha Mishra· Nov 19, 2024

    intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Anika Huang· Nov 15, 2024

    Keeps context tight: intended-vs-implemented is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Anika Choi· Nov 15, 2024

    intended-vs-implemented fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Daniel Huang· Nov 7, 2024

    We added intended-vs-implemented from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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