coderabbit-request
Skill2 starsChanged 10 days agoMITView on GitHub
Use after completing file changes - strongest for source code (AST-aware linting, security, tests), lighter support for markdown/config. Dispatches CodeRabbit reviewer subagent via the gob background runner. ALWAYS request review before considering work complete.
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The file
We split this skill into the rules it gives an agent
8 rules. About 0 are common, 6 are distinctive, and 2 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈6
Distinctive · estimate
This author's own choices. Worth a look.
2
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 17
When to use: After making ANY file changes (code, skills, documentation, config), before considering work complete.
- Distinctive: line 21
After completing changes, invoke this skill:
- Distinctive: line 47
Document in your mind what was just implemented.
- Distinctive: line 65
When CodeRabbit completes, parse the output into this JSON structure:
- Very specific: line 80
All issues have file, line, issue, and suggestion
- Distinctive: line 127
Structure: Always return JSON with three severity levels, even if some are empty arrays.
- Very specific: line 147
DO: Use --prompt-only flag for efficiency
- Distinctive: line 151
DON'T: Add your own issues
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Requesting Review
Overview
This skill dispatches a CodeRabbit reviewer subagent to analyze your uncommitted changes and return a structured list of issues. It's the first step in the change → review → triage → fix pipeline.
When to use: After making ANY file changes (code, skills, documentation, config), before considering work complete.
CRITICAL: Always Request Review
After completing changes, invoke this skill:
- ✅ Code files (.ts, .vue, .js, etc.) - strongest support (AST-aware linting, security, auto-tests)
- ✅ Test files - strongest support
- ✅ Skill files (SKILL.md) - lighter support (markdownlint)
- ✅ Documentation (.md files) - lighter support (markdownlint)
- ✅ Configuration files - lighter support (generic config linting)
Do NOT skip review because "it's just documentation" or "it's a small change". Even lighter analysis catches issues.
Outputs: Structured JSON with categorized issues (Critical/Important/Minor).
Process
Step 1: Prepare Context
Stage all changes before review. The --type uncommitted flag analyzes both staged and unstaged changes, but staging ensures nothing is missed:
# Stage all changes
git add -A
# Verify staged changes
git diff --cached --statDocument in your mind what was just implemented. This context helps the reviewer subagent understand scope.
Step 2: Run CodeRabbit via gob
CRITICAL: Always use gob add for CodeRabbit commands - they take 1-3 minutes.
# Start CodeRabbit in background
gob add coderabbit --prompt-only --type uncommitted
# Continue working or wait for results
gob await-anyThe --prompt-only flag makes output succinct and token-efficient.
Step 3: Parse Results
When CodeRabbit completes, parse the output into this JSON structure:
{
"critical": [
{"file": "...", "line": NNN, "issue": "...", "suggestion": "..."}
],
"important": [...],
"minor": [...]
}Include only genuine issues CodeRabbit found. Do not invent.
Verify:
- All issues have
file,line,issue, andsuggestion - Issues are correctly categorized by severity
- No invented issues
- CodeRabbit's exact wording preserved
Step 4: Return Results
Pass structured issue list to next skill (coderabbit-triage).
Example Output
{
"critical": [
{
"file": "src/webhooks.ts",
"line": 42,
"issue": "Missing HMAC signature verification for webhook authenticity",
"suggestion": "Add HMAC-SHA256 signature verification using crypto module. Compare against timestamp to prevent replay attacks."
}
],
"important": [
{
"file": "src/webhooks.ts",
"line": 89,
"issue": "Race condition in concurrent webhook handling. Multiple async calls can update state simultaneously.",
"suggestion": "Implement idempotency key handling. Store processed keys in cache with TTL to deduplicate retries."
},
{
"file": "src/webhooks.ts",
"line": 156,
"issue": "Missing error logging for webhook failures. Makes debugging production issues difficult.",
"suggestion": "Add structured logging with timestamp, error type, and webhook ID. Include full error stack."
}
],
"minor": []
}Key Principles
Objectivity: CodeRabbit is the source of truth. Don't filter or reinterpret findings.
Completeness: Return all issues CodeRabbit found, regardless of difficulty.
Clarity: Preserve CodeRabbit's exact issue descriptions and suggestions.
Structure: Always return JSON with three severity levels, even if some are empty arrays.
Common Patterns
When CodeRabbit finds nothing: Return empty arrays for all severity levels.
{
"critical": [],
"important": [],
"minor": []
}When issues span multiple files: Group by file but preserve line numbers.
When suggestion is vague: Include CodeRabbit's exact wording. Don't interpret or improve it.
Constraints
- DO: Use
--prompt-onlyflag for efficiency - DO: Let CodeRabbit run to completion
- DO: Preserve exact issue descriptions from CodeRabbit output
- DON'T: Filter out any findings
- DON'T: Add your own issues
- DON'T: Modify or improve CodeRabbit's suggestions
Integration Points
This skill feeds directly into coderabbit-triage:
coderabbit-request
↓ (outputs JSON)
coderabbit-triage
↓ (outputs task plan)
coderabbit-fix (parallel or sequential)Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

