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.
npx skills add opendatahub-io/ai-helpers --skill doc-reviewgit clone --depth 1 https://github.com/opendatahub-io/ai-helpersWrote 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.
[](https://agentmods.dev/skills/opendatahub-io/ai-helpers/doc-review)<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.
<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>- NVIDIA SkillSpector warn
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.
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.
| Model | Per session | Once 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 |
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.
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.jsonshould exist for cross-reference checking
Parse arguments
$ARGUMENTS contains:
- Target: file path, directory, or glob pattern for AsciiDoc files to review
- --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,.javafiles — authoritative for API behavior - API specs:
*_types.go,*.yamlCRD files — authoritative for field names and schemas - Architecture docs:
.mdfiles from architecture repos — authoritative for design - Existing docs:
.adocfiles — reference for style and terminology
Step 3: Review each file
For each AsciiDoc file, read its content and construct a review prompt combining:
- Read
${CLAUDE_SKILL_DIR}/prompts/review-content.mdtemplate - The documentation content being reviewed
- Relevant context files (matched by topic/component)
- 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
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.
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.
- 12d ago First seen · 108 lines · 41 tokens per session scan A 8c24c860b713
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.
Other skills, from other repositories
code-review-excellence
Master effective code review practices to provide constructive feedback, catch bugs early, and foster knowledge sharing while maintaining team morale. Use when reviewing pull requests, establishing review standards, or mentoring developers.
multi-reviewer-patterns
Coordinate parallel code reviews across multiple quality dimensions with finding deduplication, severity calibration, and consolidated reporting. Use this skill when organizing multi-reviewer code reviews, calibrating finding severity, or consolidating review results.
miru
Use Miru Code Search when the user asks where code lives, how behavior is wired, or what related code paths exist in a repo. Prefer this for conceptual code exploration over grep, glob, or broad file reads.
codex
Delegate coding tasks to the OpenAI Codex CLI for features, refactoring, PR reviews, and batch fixes. Requires the codex CLI and typically a git repository.
code-review
Use when asked to review code. Triggers - "look at this PR", "review this diff", "safe to merge?", change inspection. Input is a diff/branch/PR; output is a severity-ranked list of findings at file:line. If the job is to change code, use development.
code-review
This skill reviews completed implementation before it becomes part of the project's engineering history.