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 agentmods add skills/femto/minion/code-reviewnpx skills add femto/minion --skill code-reviewgit clone --depth 1 https://github.com/femto/minionWrote 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/femto/minion/code-review)<a href="https://agentmods.dev/skills/femto/minion/code-review"><img src="https://agentmods.dev/badge/skills/femto/minion/code-review.svg" alt="Measured on agentmods" 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 | $0.00040 | $0.00862 |
| Opus 5 | $0.00020 | $0.00431 |
| Sonnet 5 | $0.00008 | $0.00172 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
code-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 4d 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 Skill
Description
This skill performs comprehensive code reviews focusing on best practices, security vulnerabilities, performance optimization, and code maintainability. It can review individual files, pull requests, or entire modules.
Usage Instructions
When a user requests a code review:
- Understand the context: Identify the language, framework, and purpose of the code
- Check for security issues: Look for common vulnerabilities (OWASP Top 10, injection, XSS, etc.)
- Evaluate performance: Identify inefficient patterns, N+1 queries, memory leaks
- Review code quality: Check naming conventions, code structure, DRY principles
- Assess maintainability: Evaluate readability, documentation, test coverage
- Provide actionable feedback: Give specific suggestions with examples
Review Categories
Security Review
- SQL/Command injection vulnerabilities
- Cross-site scripting (XSS)
- Authentication and authorization issues
- Sensitive data exposure
- Insecure dependencies
- Input validation gaps
Performance Review
- Algorithm complexity (Big O)
- Database query optimization
- Memory management
- Caching opportunities
- Async/concurrent processing
- Resource cleanup
Code Quality Review
- Naming conventions
- Function/method length
- Code duplication (DRY)
- Single responsibility principle
- Error handling patterns
- Logging and debugging
Maintainability Review
- Code readability
- Documentation quality
- Test coverage
- Dependency management
- Configuration handling
- Breaking change risks
Example Prompts
- "Review this pull request for security issues"
- "Check this function for performance problems"
- "Audit this module for best practices"
- "Review my implementation and suggest improvements"
- "Find potential bugs in this code"
- "Check if this code follows SOLID principles"
Output Format
Code review results should include:
- Summary: Overall assessment (severity: critical/high/medium/low)
- Issues Found: List of problems with:
- File and line number
- Category (security/performance/quality/maintainability)
- Severity level
- Description of the issue
- Suggested fix with code example
- Positive Aspects: What's done well
- Recommendations: Prioritized list of improvements
What ships with it
1 file 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.
- 4d ago First seen · 128 lines · 40 tokens per session scan A eb553653cf24
code-review is a skill published in the GitHub repository femto/minion (149 stars, last pushed 7d ago), licensed MIT. It adds 40 tokens to every session and 862 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 2026-08-30.
Other skills, from other repositories
code-review
Comprehensive code review for security, performance, and best practices.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
test-driven-development
TDD: enforce RED-GREEN-REFACTOR, tests before code.
common-feedback-reporter
Pre-write audit for skill violations: checks planned code against loaded skill anti-patterns before any file write. Use when writing Flutter/Dart/TS code or editing SKILL.md files with active project skills. Load as composite; on auto-fixed violation, also load +common/common-learning-log.
common-exploit-verification
Enforce "No Exploit, No Report" policy with PoC construction standards, false-positive filtering, and evidence collection per vulnerability class across backend, frontend, and mobile. Use when validating security findings, constructing exploit proofs, filtering false positives, or writing pentest findings.
common-session-retrospective
Analyze conversation corrections to detect skill gaps and prepare targeted skill-library maintenance tasks. Use after any session with user corrections, rework, or retrospective requests. After finding correction loops, also load +common/common-learning-log to persist mistake entries to AGENTSLEARNING.md.