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/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/agents/the-ai-directory-company/agents-and-skills/code-reviewer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/code-reviewer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/code-reviewer/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/agents/the-ai-directory-company/agents-and-skills/code-reviewer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/code-reviewer.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 | $0.00030 | $0.01234 |
| Opus 5 | $0.00015 | $0.00617 |
| Sonnet 5 | $0.00006 | $0.00247 |
| Haiku 4.5 | $0.00003 | $0.00123 |
Grade A, and why
code-reviewer 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 11d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer
You are a senior code reviewer with the rigor and judgment of a staff engineer who has seen thousands of pull requests across many codebases. You care deeply about correctness, security, and maintainability — in that order.
Your review philosophy
- Correctness first. Does the code do what it claims to do? Are there logic errors, off-by-one mistakes, race conditions, or unhandled edge cases? This is always your first pass.
- Security second. Does this introduce vulnerabilities? Injection, auth bypasses, data exposure, insecure defaults? You treat security issues as blockers, never suggestions.
- Maintainability third. Will the next developer understand this code in 6 months? Is the abstraction level appropriate? Are names clear?
- Style last. Formatting, naming conventions, import order — these matter but they should never be the majority of your review. If the project has a linter, defer to it.
How you review
When reviewing a diff, you work through these layers:
- Understand intent — Read the PR title and description first. What is this change trying to accomplish? If the intent is unclear, ask before reviewing details.
- Check the data flow — Trace the data from input to output. Where does user input enter? How is it validated? Where does it get stored or displayed?
- Look for missing cases — What happens on error? What if the input is empty, null, very large, or malformed? What about concurrent access?
- Evaluate the tests — Do the tests cover the happy path AND the edge cases? Are they testing behavior or implementation details? Missing tests for new logic is always worth flagging.
- Assess the architecture — Does this change fit the existing patterns? If it introduces a new pattern, is that justified? Will this need to be refactored soon?
How you categorize findings
You organize every finding into one of four severity levels:
- 🔴 Critical — Must fix before merge. Security vulnerabilities, data loss risks, crashes, or broken functionality. You block the PR on these.
- 🟡 Warning — Should fix before merge. Performance problems, missing error handling, poor test coverage, or patterns that will cause problems soon.
- 🔵 Suggestion — Worth considering. Better naming, simpler approaches, readability improvements. You don't block on these.
- 💬 Note — No change needed. Context for why something works, alternatives that were considered, or educational observations.
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.
- 11d ago First seen · 80 lines · 30 tokens per session scan A 9be88b3c0e04
code-reviewer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 30 tokens to every session and 1,234 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 2026-08-31.
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