doc-health

A documentation audit that compares written instructions and examples with how the code actually works.

In plain words
What is it for?
It is for checking READMEs and other documentation for accuracy, completeness, links, setup details, and organization.
Why use it?
It helps find outdated or missing information, broken links, incorrect examples, and configuration instructions that no longer match the project.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/hatmanstack/ragstack-lambda/doc-health
Any agent
npx skills add HatmanStack/RAGStack-Lambda --skill doc-health
Clone the repo
git clone --depth 1 https://github.com/HatmanStack/RAGStack-Lambda

Made for: Claude Code, Codex.

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,284 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00037 $0.01284
Opus 5 $0.00018 $0.00642
Sonnet 5 $0.00007 $0.00257
Haiku 4.5 $0.00004 $0.00128

Measured yesterday against content hash 9b03506539ff, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

doc-health scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.claude/skills/doc-health/SKILL.md · 160 lines

How it starts

The opening of the file, as written. The whole thing — 160 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Documentation Health Audit

You coordinate a documentation drift audit of a codebase. The doc auditor runs as a separate agent with its own context window.

Input

$ARGUMENTS is optional context — the repo path, specific docs to focus on, or scope constraints. If empty, audit the current working directory.

Process

Step 1: Scope the Audit

Ask scoping questions one at a time, preferring multiple choice. Wait for each answer before asking the next.

The doc audit runs 6 detection phases: discovery, comparison (drift/gaps/stale), code examples, link integrity, config/environment, and structure. It compares documentation claims against actual code behavior.

Question 1 — Known pain points give the auditor a starting hypothesis:

Are there parts of the documentation you already know are wrong or outdated?
Stale READMEs, broken examples, missing API docs, etc.

A) Yes (tell me which docs and what's wrong)
B) No — scan everything with fresh eyes

Question 2 — Scope and constraints in one question:

What documentation should I audit, and is anything off-limits?

A) All docs, no constraints
B) All docs, but skip specific files (tell me which)
C) Specific directories only (tell me which)
D) README and API docs only

Question 3 — Language stack determines which auto-generation tools are available (typedoc for TS, sphinx for Python, swagger for REST APIs):

What's the primary language stack?

A) JS/TS — typedoc, swagger-jsdoc available
B) Python — sphinx, mkdocstrings available
C) Both

Question 4 — Prevention tooling. What automated checks to add so documentation drift becomes a CI failure instead of a periodic cleanup:

What drift prevention tooling should I add after fixing the docs?

A) Markdown linting (markdownlint) + link checking (lychee) — catches formatting issues and broken links on every PR
B) Auto-generated API docs (typedoc/sphinx) — single source of truth lives in code, not prose
C) Both A and B
D) None — just fix the existing docs, no new tooling

Read the full file on GitHub · 160 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. yesterday First seen · 160 lines · 37 tokens per session scan A 9b03506539ff

Subscribe to this mod's changes

doc-health is a skill published in the GitHub repository HatmanStack/RAGStack-Lambda (25 stars, last pushed 4d ago), licensed Apache-2.0. It adds 37 tokens to every session and 1,284 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

babysit-pr

Babysit a GitHub pull request after creation by continuously polling review comments, CI checks/workflow runs, and mergeability state until the PR is merged/closed or user help is required. Diagnose failures, retry likely flaky failures up to 3 times, auto-fix/push branch-related issues when appropriate, and keep…

openai/codex · 114 tokens

imagegen

Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output…

openai/codex · 113 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

next-cache-components-optimizer

Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…

vercel/next.js · 170 tokens