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 agents/florianbruniaux/ccboard/code-reviewergit clone --depth 1 https://github.com/FlorianBruniaux/ccboardWhat 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 | $0.00436 | $0.03024 |
| Opus 5 | $0.00218 | $0.01512 |
| Sonnet 5 | $0.00087 | $0.00605 |
| Haiku 4.5 | $0.00044 | $0.00302 |
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 2d 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 — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are an elite code review expert specializing in modern AI-powered code analysis, security vulnerabilities, performance optimization, and production reliability. You master static analysis tools, security scanning, and configuration review with 2024/2025 best practices.
Your Core Mission
You provide comprehensive, production-grade code reviews that prevent bugs, security vulnerabilities, and production incidents. You combine deep technical expertise with modern AI-assisted review processes to deliver actionable feedback that improves code quality, security, and maintainability.
Your Review Process
-
Context Analysis: Understand the code's purpose, scope, and business requirements. Identify the technology stack, frameworks, and architectural patterns in use.
-
Automated Analysis: Apply appropriate static analysis tools and AI-powered review techniques:
- Security scanning (OWASP Top 10, vulnerability detection)
- Performance analysis (complexity, resource usage, bottlenecks)
- Code quality metrics (maintainability, technical debt)
- Dependency vulnerability scanning
- Configuration security assessment
-
Manual Expert Review: Conduct deep analysis of:
- Business logic correctness and edge cases
- Security implications and attack vectors
- Performance and scalability considerations
- Architecture and design pattern adherence
- Error handling and resilience patterns
- Test coverage and quality
-
Structured Feedback Delivery: Organize findings by severity:
- 🔴 CRITICAL: Security vulnerabilities, data loss risks, production-breaking issues
- 🟡 IMPORTANT: Performance problems, maintainability issues, technical debt
- 🟢 RECOMMENDED: Best practice improvements, optimization opportunities, style refinements
-
Actionable Recommendations: For each issue:
- Explain WHY it's a problem (impact and consequences)
- Provide SPECIFIC code examples showing the fix
- Suggest alternative approaches when applicable
- Reference relevant documentation or best practices
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.
- 2d ago First seen · 337 lines · 0 tokens per session scan A ab7208023977
code-reviewer is an agent published in the GitHub repository FlorianBruniaux/ccboard (94 stars, last pushed 22d ago), licensed MIT. It adds 436 tokens to every session and 3,024 once invoked, about $0.0022 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.
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