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.
git clone --depth 1 https://github.com/vignesh2027/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/commands/vignesh2027/ai-agent-skills/review)<a href="https://agentmods.dev/commands/vignesh2027/ai-agent-skills/review"><img src="https://agentmods.dev/badge/commands/vignesh2027/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/commands/vignesh2027/ai-agent-skills/review"><img src="https://agentmods.dev/badge/commands/vignesh2027/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 | $0.00000 | $0.00158 |
| Opus 5.5 | $0.00000 | $0.00063 |
| Sonnet 5.5 | $0.00000 | $0.00032 |
| Haiku 4.5 | $0.00000 | $0.00016 |
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
Load agents/code-reviewer.md, skills/security-and-hardening/SKILL.md, and skills/performance-optimization/SKILL.md.
Conduct a thorough review of the code or PR described. Review all five dimensions:
- Correctness — Does it work correctly for all cases?
- Security — Are there vulnerabilities?
- Performance — Are there obvious inefficiencies?
- Readability — Is it clear and maintainable?
- Architecture — Does it fit the design?
For each issue: specify dimension, severity (Blocking/Major/Minor/Nit), location, what's wrong, why it matters, and a specific fix.
Do not approve code with Blocking issues. Blocking issues: security vulnerabilities, correctness failures, missing tests.
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 · 14 lines · 0 tokens per session scan A 3a29da0a0df5
review is a command published in the GitHub repository vignesh2027/AI-AGENT-SKILLS (2 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 158 tokens. 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.
Other commands, from other repositories
speckit.critique.run
Perform a dual-lens critical review of the specification and plan from both product strategy and engineering risk perspectives before implementation.
mach12:issue-review
Read a GitHub issue and all comments, review the implementation plan, and present findings.
mach12:pr-review-assessment
Independently assess each finding from a PR review and classify it.
mach12:gh-pr-read
Read a GitHub pull request's title, body, and all top-level PR conversation comments; optionally locate an HTML-marker comment.
code-review
A structured review of code changes using four checks: risk, resilience, readability, and reliability. It produces a review report for the change.
review-code
Agent review pass plus manual checklist for correctness, security, performance, and accessibility.