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 latestaiagents/agent-skills --skill ai-code-reviewergit clone --depth 1 https://github.com/latestaiagents/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/latestaiagents/agent-skills/ai-code-reviewer)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/ai-code-reviewer"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/ai-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/skills/latestaiagents/agent-skills/ai-code-reviewer"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/ai-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 | $0.00062 | $0.01695 |
| Opus 5 | $0.00031 | $0.00847 |
| Sonnet 5 | $0.00012 | $0.00339 |
| Haiku 4.5 | $0.00006 | $0.00169 |
Grade A, and why
ai-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 9d 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 — 303 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Code Reviewer
Systematically review AI-generated code to catch common mistakes before they hit production.
When to Use
- After AI generates code you plan to use
- Before committing AI-assisted changes
- When AI code "looks right" but you want verification
- Reviewing PRs with significant AI-generated content
The AI Code Review Checklist
1. Correctness Issues (Most Common)
AI often generates code that looks correct but has subtle bugs.
Check for:
-
Hallucinated APIs - Methods/functions that don't exist
// AI might generate: array.findLast(x => x.id === id) // Verify this exists in your target -
Wrong library versions - API changes between versions
// React 18 vs 19 differences // Node.js API differences -
Off-by-one errors - Loop bounds, array indices
for (let i = 0; i <= arr.length; i++) // Should be < -
Incorrect null/undefined handling
user.profile.name // What if profile is undefined?
2. Security Issues
AI doesn't prioritize security unless explicitly asked.
Check for:
-
SQL Injection
// BAD: AI might generate db.query(`SELECT * FROM users WHERE id = ${userId}`) // GOOD: Parameterized db.query('SELECT * FROM users WHERE id = $1', [userId]) -
XSS Vulnerabilities
// BAD: Direct HTML insertion element.innerHTML = userInput // GOOD: Escaped or use framework element.textContent = userInput -
Exposed Secrets
// AI might hardcode values from context const API_KEY = 'sk-abc123...' // Should be env var -
Missing Input Validation
// AI often skips validation function processData(data) { return data.items.map(...) // What if data is null? }
3. Performance Issues
AI optimizes for "looks correct" not "performs well."
Check for:
- N+1 Queries
// BAD: AI loves this pattern users.forEach(async user => { const posts = await getPosts(user.id) // N queries! }) // GOOD: Batch const posts = await getPostsForUsers(userIds)
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.
- 9d ago First seen · 303 lines · 62 tokens per session scan A fc6b2a70991a
ai-code-reviewer is a skill published in the GitHub repository latestaiagents/agent-skills (5 stars, last pushed 4mo ago), licensed MIT. It adds 62 tokens to every session and 1,695 once invoked, about $0.0003 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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codebase-sync
Convention discovery and rule generation from codebase analysis. Scans project structure, builds search indexes, identifies patterns, and generates enforceable rules.
code-review-patterns
Multi-dimensional code assessment across security, quality, performance, and maintainability with confidence-gated reporting (>=80%) and Router Contract generation.
subagent-driven-development
Use when executing implementation plans with independent tasks in the current session. Dispatches fresh subagent per task.
reviewing-changes
Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with addreviewcomment, and settle with exactly one reviewverdict.
install-gemini-code-review-action
Installs, configures, or upgrades Dazbo's PR code review & issue triage GitHub Action (gemini-review-action) in a target repository. Use when the user wants to set up, configure, or update automated AI code reviews, issue triaging, or Gemini model workflows for their GitHub repository.