doc-review

doc-review is a command for coding agents from LeanAndMean/mach10. It costs 8 tokens per session (2,022 once invoked), scanned A, original, MIT.

A command for reviewing documentation against the changes in a pull request, which is a proposed change for review before it is added to a shared codebase.

In plain words
What is it for?
Use it to review documentation for a specific pull request, optionally limiting the review to an area such as API documentation or a README.
Why use it?
It helps reveal documentation that is missing, outdated, or incorrect after code changes. It can then update the documentation after you approve the findings.

Command

Part of the mach10 plugin — 14 commands, 1 agent shipped together

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 commands/leanandmean/mach10/doc-review
Clone the repo
git clone --depth 1 https://github.com/LeanAndMean/mach10

Or install mach10, the plugin that ships this one along with the rest of its 14 commands, 1 agent.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for doc-review

README.md
[![agentmods](https://agentmods.dev/badge/commands/leanandmean/mach10/doc-review.svg)](https://agentmods.dev/commands/leanandmean/mach10/doc-review)
Your own site
<a href="https://agentmods.dev/commands/leanandmean/mach10/doc-review"><img src="https://agentmods.dev/badge/commands/leanandmean/mach10/doc-review.svg" alt="Measured on agentmods" height="20"></a>
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,022 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 $0.00008 $0.02022
Opus 5 $0.00004 $0.01011
Sonnet 5 $0.00002 $0.00404
Haiku 4.5 $0.00001 $0.00202

Measured 5d ago against content hash ff9c6e041e86, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

doc-review 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 5d ago.

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.

commands/doc-review.md · 212 lines

How it starts

The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Documentation Review

You are performing a thorough review of documentation against the changes in a pull request. The goal is to identify stale, missing, or incorrect documentation, present findings for user approval, apply accepted changes, and verify the result.

User input: $ARGUMENTS

Step 0: Parse input and create task list

The user's input typically contains:

  • A PR number (required)
  • Additional context or scope to narrow the review (optional)

Example inputs:

  • 108
  • 108 focus on API docs
  • 108 only README

Extract the PR number. If context is provided, note it for filtering and focus in Step 3 onward. If the input is ambiguous, ask the user to clarify.

After parsing input, create the progress-tracking task list. Create a task for Step 0 and immediately mark it in progress. Then create tasks for each of the remaining 8 steps one at a time, in step order, all starting as pending. Task list display order matches creation order, so each task must be a separate sequential call -- do not batch multiple task creations in a single message. Store each returned task ID for later use -- do not assume IDs are sequential.

Task Subject activeForm
Step 0 Step 0: Parse input and create task list Parsing input
Step 1 Step 1: Check out PR branch Checking out PR branch
Step 2 Step 2: Gather PR context Gathering PR context
Step 3 Step 3: Discover documentation Discovering documentation
Step 4 Step 4: Fan out review agents Reviewing documentation
Step 5 Step 5: Present findings and get user decisions Processing findings with user
Step 6 Step 6: Apply documentation changes Applying changes
Step 7 Step 7: Verify changes Verifying changes
Step 8 Step 8: Commit and report Committing and reporting

Mark Step 0 complete.

Step 1: Check out PR branch

Mark Step 1 in progress.

Ensure you are on the PR's branch with the latest changes:

gh pr checkout <pr-number>
git pull

Read the full file on GitHub · 212 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. 5d ago First seen · 212 lines · 8 tokens per session scan A ff9c6e041e86

Subscribe to this mod's changes

doc-review is a command published in the GitHub repository LeanAndMean/mach10 (20 stars, last pushed 3mo ago), licensed MIT. It adds 8 tokens to every session and 2,022 once invoked, about $0.0000 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.

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