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Killer-Skills

repo-doc-audit — Categories.community

v1.0.0
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About this Skill

Ideal for Code Review Agents requiring comprehensive repository documentation audits for open-source projects like carbon accounting for cities Open Source carbon accounting for cities

Open-Earth-Foundation Open-Earth-Foundation
[0]
[0]
Updated: 3/3/2026

Quality Score

Top 5%
45
Excellent
Based on code quality & docs
Installation
SYS Universal Install (Auto-Detect)
Cursor IDE Windsurf IDE VS Code IDE
> npx killer-skills add Open-Earth-Foundation/CityCatalyst/repo-doc-audit

Agent Capability Analysis

The repo-doc-audit MCP Server by Open-Earth-Foundation is an open-source Categories.community integration for Claude and other AI agents, enabling seamless task automation and capability expansion.

Ideal Agent Persona

Ideal for Code Review Agents requiring comprehensive repository documentation audits for open-source projects like carbon accounting for cities

Core Value

Empowers agents to produce actionable reports on README.md and architecture.md accuracy, ensuring up-to-date documentation across setup, run, config, and troubleshooting sections, leveraging markdown files like README.md and architecture.md

Capabilities Granted for repo-doc-audit MCP Server

Auditing repository documentation after major refactorings
Validating README.md accuracy for setup and troubleshooting
Generating reports on architecture.md for open-source carbon accounting projects

! Prerequisites & Limits

  • Intentionally not automatic due to potential expense and noise
  • Requires manual invocation for full repo documentation audit
Project
SKILL.md
3.0 KB
.cursorrules
1.2 KB
package.json
240 B
Ready
UTF-8

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SKILL.md
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repo-doc-audit

This is a one-off, repo-wide documentation audit. It is intentionally not automatic because it can be expensive and noisy.

When to use

  • Use this skill when the user asks for a full repo documentation audit or “is everything up to date?”
  • Use after large refactors, restructurings, or major feature additions.

Goal

Produce an actionable report (and optionally fixes) across:

  • README.md accuracy (setup/run/config/troubleshooting)
  • architecture.md accuracy (diagrams and component naming)
  • Script docstrings and runnable-script conventions
  • Consistency between dependency docs (pyproject.toml, requirements.txt, and lockfiles such as uv.lock / poetry.lock)
  • Presence of .env.example entries for documented env vars

Audit method (recommended)

1) Establish repo “truth” from code and configs

  • Identify real entrypoints (commands in README.md, modules with __main__, top-level scripts).
  • Identify configuration files that control runtime (e.g., model/provider selection, pipeline toggles).
  • Identify output folders and naming conventions from code paths.

2) Read and validate key docs against truth

  • README.md
    • Commands exist and match actual flags/paths.
    • Setup instructions match dependency source of truth policy.
    • Output structure matches code behavior.
  • architecture.md
    • Components referenced exist in the repo.
    • Diagrams reflect current flow and naming.
  • Module-level READMEs (if present in modules)
    • Do they describe actual behavior and entrypoints?

3) Script/docstring audit (repo-wide)

For any file that looks runnable (heuristics):

  • Located under a scripts/ folder, OR
  • Mentioned in docs as an entrypoint, OR
  • Contains if __name__ == "__main__":, OR
  • Imports argparse and defines main()

Check:

  • Top-level module docstring exists and covers:
    • Brief
    • Inputs (each CLI flag should have a short purpose + expected format; env vars should explain what they control)
    • Outputs
    • Usage from project root (python -m ...)
  • Uses argparse for CLI (when runnable).
  • Has __main__ guard.
  • Avoids side effects at import time.
  • Logging: uses logging (not print) except intentional CLI UX.
  • Imports: prefer absolute imports.
  • Paths: prefer pathlib.Path.

Additionally (repo-wide): check that every function and method has a docstring.

  • Trivial functions/methods: one-liner docstring is acceptable.
  • Non-trivial or side-effecting functions/methods: docstring should explain inputs/outputs, side effects, and raised exceptions when non-obvious.

4) Produce an output report

Deliver a report with:

  • Summary: 3–6 bullets of highest-impact issues
  • Findings grouped by document/file
  • Fix suggestions: concrete edits (small, scoped)
  • Optional automated fixes:
    • Only apply if user asked you to fix; otherwise just report.

Non-goals

  • Do not try to enforce style consistency beyond correctness.
  • Do not rewrite large sections unless necessary for accuracy.

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