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 eigent-ai/agent-skills --skill reviewgit clone --depth 1 https://github.com/eigent-ai/agent-skillsWrote 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/eigent-ai/agent-skills/review)<a href="https://agentmods.dev/skills/eigent-ai/agent-skills/review"><img src="https://agentmods.dev/badge/skills/eigent-ai/agent-skills/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/eigent-ai/agent-skills/review"><img src="https://agentmods.dev/badge/skills/eigent-ai/agent-skills/review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00051 | $0.00398 |
| Opus 5.5 | $0.00020 | $0.00159 |
| Sonnet 5.5 | $0.00010 | $0.00080 |
| Haiku 4.5 | $0.00005 | $0.00040 |
Grade A, and why
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 1mo 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.
What it actually says
Review
Overview
Use this skill for code-review work. Prioritize bugs, behavioral regressions, security risks, performance issues, and missing tests. Findings come first, ordered by severity, with concrete file and line references whenever possible.
Workflow
- Inspect the requested diff, files, branch, or pull request context.
- Review across five axes:
- Correctness.
- Security.
- Performance.
- Readability.
- Maintainability.
- Check whether tests cover the changed behavior and important failure modes.
- Keep feedback specific and actionable; avoid broad style commentary unless it affects maintainability or correctness.
- Flag issues by severity:
- Blocking: should not ship.
- High: likely bug, security issue, or serious regression.
- Medium: meaningful risk or missing coverage.
- Low: small maintainability or clarity improvement.
- If no issues are found, say so clearly and mention residual test gaps or assumptions.
Output Pattern
- Findings first, ordered by severity.
- Open questions or assumptions.
- Brief change summary only after findings.
- Tests or verification reviewed.
Example Prompts
/review - Review this pull request. Flag anything that affects security, performance, or will be hard to maintain in 6 months. Be direct about blocking issues./review - Audit this API endpoint handler for security issues. Check for injection risks, missing auth checks, improper error handling, and data exposure./review - This is going to production tomorrow. Do a final review focused only on breaking changes, regressions, and anything that could cause an incident.
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.
- 1mo ago First seen · 42 lines · 51 tokens per session scan A 7cfe14d1cee4
review is a skill published in the GitHub repository eigent-ai/agent-skills (24 stars, last pushed 12d ago), licensed Apache-2.0. It adds 51 tokens to every session and 398 once invoked, about $0.0002 per session on Opus 5.5. 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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