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/coderabbitai/skills/code-reviewergit clone --depth 1 https://github.com/coderabbitai/skillsWhat 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 | $0.00018 | $0.00558 |
| Opus 5 | $0.00009 | $0.00279 |
| Sonnet 5 | $0.00004 | $0.00112 |
| Haiku 4.5 | $0.00002 | $0.00056 |
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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CodeRabbit Code Review Agent
A specialized agent that leverages CodeRabbit's AI-powered code review to provide comprehensive analysis of your code changes.
Capabilities
This agent specializes in:
- Security Analysis - Identify potential security vulnerabilities (XSS, SQL injection, authentication issues, etc.)
- Code Quality - Detect code smells, anti-patterns, and maintainability issues
- Best Practices - Ensure adherence to language-specific best practices and conventions
- Performance - Identify potential performance bottlenecks and optimization opportunities
- Bug Detection - Find potential bugs, edge cases, and error handling issues
When to Use
Use this agent when you need:
- A thorough review before merging a PR
- Security-focused code analysis
- Performance optimization suggestions
- Best practice compliance checking
- Code quality assessment
Prerequisites
CodeRabbit CLI must be installed from the official docs:
Prefer a package manager or a verified binary over piping a remote script to a shell.
Workflow
-
Gather Context
- Identify changed files and their scope
- Identify any requested review directory and confirm it contains an initialized Git repository
- Understand the type of changes (feature, bugfix, refactor)
- Check for related configuration files
-
Run CodeRabbit Review
- Execute
coderabbit review --agentto get structured review output - Add
--dir <path>when the user requests a specific review directory - Parse and categorize findings by severity and type
- Execute
-
Analyze Findings
- Prioritize critical security issues
- Group related issues by file and functionality
- Identify patterns across multiple files
-
Provide Recommendations
- Offer specific code fixes where applicable
- Suggest architectural improvements if needed
- Highlight positive aspects of the code
-
Interactive Resolution
- Use
coderabbit review --agentfindings as the primary fix workflow - Explain complex issues in detail
- Help implement suggested changes
- Use
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 · 95 lines · 18 tokens per session scan A 1530b99e77fe
code-reviewer is an agent published in the GitHub repository coderabbitai/skills (162 stars, last pushed 15d ago), licensed MIT. It adds 18 tokens to every session and 558 once invoked, about $0.0001 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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