doc-review

doc-review is a skill for Claude Code from opendatahub-io/ai-helpers. It costs 41 tokens per session (875 once invoked), scanned A, original, Apache-2.0.

A review tool for AsciiDoc files, a plain-text format used for technical documentation. It compares the docs with a supplied context package, such as source code and specifications.

In plain words
What is it for?
Use it to review one file, a directory, or a file pattern and save the findings as workspace/review-findings.json.
Why use it?
It helps find incorrect claims, missing information, contradictions, and made-up details before documentation is published.

Skill for Claude Code

Written for Claude Code: argument-hint in frontmatter. Also seen: model in frontmatter.

Part of the odh-documentation plugin — 9 skills shipped together

Good fit Use it to review one file, a directory, or a file pattern and save the findings as workspace/review-findings.json.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/opendatahub-io/ai-helpers/doc-review
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.

Any agent
npx skills add opendatahub-io/ai-helpers --skill doc-review
Clone the repo
git clone --depth 1 https://github.com/opendatahub-io/ai-helpers

Made for: Claude Code.

Or install odh-documentation, the plugin that ships this one along with the rest of its 9 skills.

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/skills/opendatahub-io/ai-helpers/doc-review/github.svg)](https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-review)
Your own site
<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-review"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-review/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for doc-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-review"><img src="https://agentmods.dev/badge/skills/opendatahub-io/ai-helpers/doc-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 875 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 8
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
How audits are shown
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 5.1 $0.00041 $0.00875
Opus 5 $0.00020 $0.00438
Sonnet 5 $0.00008 $0.00175
Haiku 4.5 $0.00004 $0.00088

Measured 12d ago against content hash 8c24c860b713, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, 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 12d 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.

plugins/odh-documentation/skills/doc-review/SKILL.md · 108 lines

How it starts

The opening of the file, as written. The whole thing — 108 lines — stays where its author put it; the contents beside it link to each section on GitHub.

doc-review

Perform adversarial comparison of documentation content against context sources to detect inaccuracies, omissions, and hallucinations.

Prerequisites

  • AsciiDoc files to review (specified in arguments)
  • workspace/context-package.json should exist for cross-reference checking

Parse arguments

$ARGUMENTS contains:

  1. Target: file path, directory, or glob pattern for AsciiDoc files to review
  2. --context (optional): path to context package (defaults to workspace/context-package.json)

Step 1: Discover files

Resolve the target to a list of .adoc files:

  • Single file: review that file
  • Directory: glob **/*.adoc
  • Glob pattern: expand it

Step 2: Load context

Read workspace/context-package.json and extract:

  • Ticket metadata (summary, description, components)
  • Context files with content (source code, API specs, architecture docs, existing docs)
  • Product conventions

Group context files by type for targeted comparison:

  • Source code: .go, .py, .java files — authoritative for API behavior
  • API specs: *_types.go, *.yaml CRD files — authoritative for field names and schemas
  • Architecture docs: .md files from architecture repos — authoritative for design
  • Existing docs: .adoc files — reference for style and terminology

Step 3: Review each file

For each AsciiDoc file, read its content and construct a review prompt combining:

  1. Read ${CLAUDE_SKILL_DIR}/prompts/review-content.md template
  2. The documentation content being reviewed
  3. Relevant context files (matched by topic/component)
  4. Ticket metadata

Ask the LLM to perform adversarial review:

  • Factual accuracy: Compare every technical claim against source code and API specs
  • Completeness: Check if important details from the ticket and context are covered
  • Consistency: Verify terminology matches existing documentation
  • Hallucination check: Flag any API fields, CLI flags, config options, or behaviors not found in context sources

Read the full file on GitHub · 108 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 12d ago First seen · 108 lines · 41 tokens per session scan A 8c24c860b713

Subscribe to this mod's changes

doc-review is a skill published in the GitHub repository opendatahub-io/ai-helpers (37 stars, last pushed 5d ago), licensed Apache-2.0. It adds 41 tokens to every session and 875 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.

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