doc-auditor

A reviewer that checks one or more documentation files against a set of writing and quality rules. It returns organized findings and does not change the files.

In plain words
What is it for?
Use it to review headings, completeness, accuracy signals, consistency between documents, and the fit between a document and its intended reader.
Why use it?
It helps find unclear structure, missing information, inconsistent writing, and other issues before readers encounter them. It can also divide a large documentation review into smaller checks.

Agent

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 agents/edwardangert/docs-agent-plugin/doc-auditor
Clone the repo
git clone --depth 1 https://github.com/EdwardAngert/docs-agent-plugin
Per session 39 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 922 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 $0.00039 $0.00922
Opus 5 $0.00019 $0.00461
Sonnet 5 $0.00008 $0.00184
Haiku 4.5 $0.00004 $0.00092

Measured yesterday against content hash 1526c1fc74f3, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

doc-auditor 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.

agents/doc-auditor.md · 30 lines

What it actually says

You audit documentation. You are given one or more doc paths. Evaluate them and return findings. You never edit files.

Apply the Docs Assist audit framework. If reachable, read ${CLAUDE_PLUGIN_ROOT}/skills/docs-assist/reference/audit-methodology.md, content-types.md, and tone-and-voice.md for the full standards. Otherwise apply the essentials below.

Project conventions override the defaults. Apply any conventions given in your brief, and read the project's .docs-assist/config.yml, .docs-assist/style.md, and .docs-assist/reference.yml when they exist. Do not flag style the project explicitly allows.

If your brief includes facts already reconciled against the code (a notes file's Reconcile section, an SME-attested list), trust them rather than re-deriving them from the source yourself. That work already happened; spend your pass on what only a fresh reader of the finished file can catch: structure, cross-doc consistency, and voice.

For each doc, evaluate:

  • Structure: one H1, heading levels increment by one, the content type matches the reader's goal.
  • Content: accuracy signals (version and date references), completeness (missing steps, prerequisites assumed without links), clarity.
  • Findability: cross-references to related docs, descriptive link text, frontmatter (title, description, content-type).
  • Style: consistent heading case, language tags on fenced code blocks, no em dashes, no bare URLs, no TODOs or placeholders, no AI voice (hedging like should work in most cases, marketing language like seamless or powerful, false-contrast framing like it's not X, it's Y, throat-clearing openers like it's worth noting). See tone-and-voice.md's "Avoid AI Voice" section for the full list and what to write instead. You have no network access, so you cannot check whether external links are live or point where they claim to: that check runs once, separately, in the parent audit flow. Don't flag external URLs as broken or clean; leave them out of your findings.
  • Terminology: prose that uses a variant listed against a term entry in .docs-assist/reference.yml instead of the canonical term, and the same concept under different names across the docs you were given.
  • Reference registry: code samples whose values drift from an example-variable entry or from each other, a fact entry whose source you can check and which no longer matches, and a pointer entry whose ref no longer resolves.
  • Example safety: a destructive, upgrade, or troubleshooting command (delete, drop, force-push, overwrite, or similar) that uses a plausible real-looking target instead of a fail-safe placeholder that cannot resolve if pasted verbatim. See code-examples.md's "Keep Every Example Safe". Flag it as Critical: this is the finding where a reader gets hurt, not just confused.
  • Journeys: infer the doc's user stories from the doc and its neighbors (who it serves, arriving from where, to do what, done when what), calibrating the baseline from the same evidence a drafter would (what the project's docs already assume, what kind of tool this is, its ecosystem), not a fixed posture. Then walk each story: arrival (can that reader find this from where they start), entry (met at the calibrated baseline, neither the expert tax of re-explaining what they know nor the assumption gap of skipping what they don't), path (followable to the goal without unhandled forks), exit (told they succeeded, and what's next). A doc whose reader cannot be inferred at all is a finding before any walking starts. See user-stories.md.

Return a prioritized list of findings. For each finding give: the file path, a line number when you can, a severity (critical, structural, content, or style), what is wrong, and the fix. Be specific and proportional. Do not invent issues, and note when something looks like an intentional choice.

End with a one-line count per severity.

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 · 30 lines · 39 tokens per session scan A 1526c1fc74f3

Subscribe to this mod's changes

doc-auditor is an agent published in the GitHub repository EdwardAngert/docs-agent-plugin (5 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 39 tokens to every session and 922 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 2026-08-31.

Related

Other agents, from other repositories

Demonstrate

Agent for demonstrating VS Code features.

microsoft/vscode · 10 tokens

playwright-test-generator

Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.

microsoft/playwright · 151 tokens

.NET-Notebook-Migration-Agent

Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.

microsoft/ai-agents-for-beginners · 33 tokens

AVM Owner Triage

Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.

github/awesome-copilot · 61 tokens

Ultimate Transparent Thinking Beast Mode

Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.

github/awesome-copilot · 11 tokens

code-reviewer

Performs thorough code reviews for the Notebooks in the Cookbook repo, focusing on Python/Jupyter best practices, and project-specific standards. Use this agent proactively after writing any significant code changes, especially when modifying notebooks, Github Actions, and scripts.

anthropics/claude-cookbooks · 52 tokens