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 The-AI-Directory-Company/agents-and-skills --skill code-review-checklistgit clone --depth 1 https://github.com/The-AI-Directory-Company/agents-and-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/the-ai-directory-company/agents-and-skills/code-review-checklist)<a href="https://agentmods.dev/skills/the-ai-directory-company/agents-and-skills/code-review-checklist"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/code-review-checklist/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/the-ai-directory-company/agents-and-skills/code-review-checklist"><img src="https://agentmods.dev/badge/skills/the-ai-directory-company/agents-and-skills/code-review-checklist.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 | $0.00033 | $0.01445 |
| Opus 5 | $0.00016 | $0.00723 |
| Sonnet 5 | $0.00007 | $0.00289 |
| Haiku 4.5 | $0.00003 | $0.00145 |
Grade A, and why
code-review-checklist 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 8d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Checklist
Before you start
Gather the following from the reviewer or PR author. If anything is missing, ask before proceeding:
- What does the PR do? — The specific change, not "fixes stuff"
- Why is it needed? — Link to ticket, bug report, or requirement
- What is the blast radius? — Which systems, APIs, or users are affected
- Are there tests? — New, modified, or explicitly skipped (with justification)
- Is there a deploy plan? — Feature flag, migration, rollback strategy
Review template
1. First Pass: Understand Intent
Read the PR description and linked ticket before touching code. Answer:
- What is the expected behavior change?
- What should NOT change?
- Are there edge cases called out by the author?
If the PR description is empty or vague, stop and request one. Reviewing code without context produces shallow feedback.
2. Correctness
Check each item:
- Logic matches the stated requirement — not just "runs without errors"
- Edge cases are handled: empty inputs, null values, boundary conditions, concurrent access
- Error paths return meaningful messages, not silent failures or generic 500s
- State mutations are atomic where required — no partial updates on failure
- Data types are correct: string vs number, nullable vs required, timezone-aware dates
3. Security
- User input is validated and sanitized before use
- Authentication and authorization checks are present on new endpoints
- Secrets are not hardcoded or logged — check for API keys, tokens, passwords
- SQL queries use parameterized statements, not string concatenation
- File uploads validate type, size, and content — not just extension
- CORS, CSP, and rate limiting are configured for new routes
4. Performance
- Database queries use indexes — check for full table scans on large tables
- N+1 queries are eliminated — look for loops that issue individual queries
- Large datasets are paginated, not loaded entirely into memory
- Expensive computations are cached or debounced where appropriate
- New API endpoints have timeout and retry policies defined
What ships with it
3 files 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.
- 8d ago First seen · 128 lines · 33 tokens per session scan A 34fb06d64b27
code-review-checklist is a skill published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 33 tokens to every session and 1,445 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 2026-09-03.
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reflecting-findings
Use when a reflection package hands you another agent's review findings to verify (before they become a fix request): you are the REFLECTOR, an independent skeptic. Judge each finding against the real code and settle it with reflectfinding — kept or refuted.
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generic-static-code-reviewer
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