How do I install the Review Documentation skill?

Run npx skillmds@latest add charly-vibes/review-documentation in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

What does the Review Documentation skill do?

Review Documentation Quality It is listed under Docs & Writing on SkillMD.

Is Review Documentation safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Review Documentation?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Review Documentation free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Review Documentation?

charly-vibes (@charly-vibes) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Review Documentation by charly-vibes

Name: Review Documentation
Author: charly-vibes

File contents

Review Documentation Quality

Perform a Rule of 5 review of the documentation using the Unified Frameworks for Technical Information Architecture.

Core Rules

You are a reviewer, not a writer. Flag issues with locations; do not rewrite content.

Procedure

Perform 5 iterative passes. Check for convergence after each pass starting with Pass 2: report new CRITICAL issues, total new issues, the change vs. the previous pass, and status (CONVERGED or CONTINUE). Converge when no new CRITICAL issues are found and the new-issue rate drops below 10% versus the previous pass. Stop early if converged.

Pass 1: Diátaxis & Intent

Goal: does this topic have a single, clear intent? Checks: is it a Tutorial, How-to, Reference, or Explanation? Are there leaked intents (e.g., too much theory in a Tutorial)? Issue prefix: [INTENT-001].

Pass 2: EPPO & Standalone Utility

Goal: can a foraging reader land here and succeed? Checks: are context and prerequisites established in the first two sentences? Does it rely on "previous chapter" knowledge without links? Issue prefix: [EPPO-001].

Pass 3: Cognitive Scannability (Info Mapping)

Goal: can the reader scan the page in under 10 seconds and find the key answer? Checks: are descriptive labels used for every 2-3 paragraphs? Are paragraphs chunked into small units? Are tables and lists used for complex data? Issue prefix: [SCAN-001].

Pass 4: Accuracy, "Whole Game" & Fail-States

Goal: is the content technically sound and resilient? Checks: are code snippets verified? Does it show the complete result? Are common errors documented? In tutorials, is the Martini Glass pattern followed (guided context then open exploration)? Issue prefix: [ACC-001].

Pass 5: Excellence & AI-Readiness

Goal: is it production-ready and optimized for AI agents? Checks: is there a TL;DR for LLMs? Are headers semantically unique for RAG? Is the Answer First (Pyramid Principle) in the TL;DR and first paragraph? Use references/ai-readiness-criteria.md for the deep AI-readiness checks (llms.txt, RAG chunking, dual-audience, schema sync). Issue prefix: [EXCL-001].

Final Report

Total Issues by Severity (Critical, High, Medium, Low).
Top 3 Findings.
Verdict: Ready, Needs Revision, or Needs Rework.
Rationale.

Rules

Reference exact locations (file, section, paragraph).
Validate issues exist; do not flag "potential" issues without evidence.
The canonical theory lives in content/research-documentation-frameworks.md; references/ai-readiness-criteria.md is an operational excerpt that links back to it.

Related Skills

research-documentation — architecture-level gaps.
implement-documentation — fixing what the review finds.

---name: review-documentationdescription: Review Documentation Quality---# Review Documentation QualityPerform a Rule of 5 review of the documentation using the Unified Frameworks for Technical Information Architecture.## Core Rules- You are a reviewer, not a writer. Flag issues with locations; do not rewrite content.## ProcedurePerform 5 iterative passes. Check for convergence after each pass starting with Pass 2: report new CRITICAL issues, total new issues, the change vs. the previous pass, and status (CONVERGED or CONTINUE). Converge when no new CRITICAL issues are found and the new-issue rate drops below 10% versus the previous pass. Stop early if converged.### Pass 1: Diátaxis & IntentGoal: does this topic have a single, clear intent? Checks: is it a Tutorial, How-to, Reference, or Explanation? Are there leaked intents (e.g., too much theory in a Tutorial)? Issue prefix: [INTENT-001].### Pass 2: EPPO & Standalone UtilityGoal: can a foraging reader land here and succeed? Checks: are context and prerequisites established in the first two sentences? Does it rely on "previous chapter" knowledge without links? Issue prefix: [EPPO-001].### Pass 3: Cognitive Scannability (Info Mapping)Goal: can the reader scan the page in under 10 seconds and find the key answer? Checks: are descriptive labels used for every 2-3 paragraphs? Are paragraphs chunked into small units? Are tables and lists used for complex data? Issue prefix: [SCAN-001].### Pass 4: Accuracy, "Whole Game" & Fail-StatesGoal: is the content technically sound and resilient? Checks: are code snippets verified? Does it show the complete result? Are common errors documented? In tutorials, is the Martini Glass pattern followed (guided context then open exploration)? Issue prefix: [ACC-001].### Pass 5: Excellence & AI-ReadinessGoal: is it production-ready and optimized for AI agents? Checks: is there a TL;DR for LLMs? Are headers semantically unique for RAG? Is the Answer First (Pyramid Principle) in the TL;DR and first paragraph? Use `references/ai-readiness-criteria.md` for the deep AI-readiness checks (`llms.txt`, RAG chunking, dual-audience, schema sync). Issue prefix: [EXCL-001].## Final Report- Total Issues by Severity (Critical, High, Medium, Low).- Top 3 Findings.- Verdict: Ready, Needs Revision, or Needs Rework.- Rationale.## Rules- Reference exact locations (file, section, paragraph).- Validate issues exist; do not flag "potential" issues without evidence.- The canonical theory lives in `content/research-documentation-frameworks.md`; `references/ai-readiness-criteria.md` is an operational excerpt that links back to it.## Related Skills- `research-documentation` — architecture-level gaps.- `implement-documentation` — fixing what the review finds.

Frequently asked questions

Review Documentation