Coderabbit Performance Tuning

CodeRabbit Performance Tuning

Overview

Optimize CodeRabbit review speed, relevance, and developer experience. Review time is primarily a function of PR size. Comment quality is controlled by profile selection, path instructions, and learnings. This skill covers all the levers for tuning CodeRabbit to your team's needs.

Prerequisites

• CodeRabbit installed and producing reviews
• .coderabbit.yaml in repository root
• Several PRs worth of review history to evaluate

Performance Factors

Instructions

Step 1: Optimize PR Size for Faster Reviews

# PR size directly impacts review speed and quality

| PR Size | Review Time | Review Quality |
|---------|------------|----------------|
| < 200 lines | 2-3 min | Excellent -- focused, actionable |
| 200-500 lines | 3-7 min | Good -- catches most issues |
| 500-1000 lines | 7-12 min | Moderate -- may miss nuanced issues |
| 1000+ lines | 12-15+ min | Low -- too much context |

# Enforce PR size limits with CI:

# .github/workflows/pr-size.yml
name: PR Size Check
on: [pull_request]
jobs:
  check:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
        with:
          fetch-depth: 0
      - name: Check PR size
        run: |
          TOTAL=$(git diff --stat origin/${{ github.base_ref }}...HEAD | tail -1 | \
            grep -oP '\d+ insertion|d+ deletion' | grep -oP '\d+' | \
            awk '{sum+=$1} END {print sum+0}')
          echo "Lines changed: $TOTAL"
          if [ "$TOTAL" -gt 500 ]; then
            echo "::warning::Large PR ($TOTAL lines). Consider splitting for better CodeRabbit review quality."
          fi

Step 2: Choose the Right Review Profile

# .coderabbit.yaml - Profile comparison
reviews:
  profile: "assertive"    # Start here, tune based on team feedback

# Profile decision guide:
#
# "chill":
#   - 1-3 comments per PR
#   - Only critical issues and bugs
#   - Best for: senior teams, high-trust environments
#   - Warning: may miss moderate issues
#
# "assertive" (recommended):
#   - 3-8 comments per PR
#   - Bugs, security, best practices
#   - Best for: most teams
#   - Good balance of signal-to-noise
#
# Tune based on metrics:
#   - Team ignoring most comments? → Switch to chill
#   - Security issues slipping through? → Stay on assertive
#   - New or junior team? → assertive catches more learning opportunities

Step 3: Add Path Instructions for Relevance

# .coderabbit.yaml - Context makes reviews more relevant
reviews:
  path_instructions:
    # Tell CodeRabbit WHAT to look for (increases relevance)
    - path: "src/api/**"
      instructions: |
        Review for: input validation, proper HTTP status codes, auth middleware.
        Ignore: import order, logging format.

    - path: "src/components/**"
      instructions: |
        Review for: accessibility (aria labels), performance (memo/useMemo).
        Ignore: CSS naming, component file structure.

    - path: "**/*.test.*"
      instructions: |
        Review for: assertion completeness, edge cases, async handling.
        Do NOT comment on: test naming conventions, import order.

    # Tell CodeRabbit what NOT to comment on (reduces noise)
    - path: "src/legacy/**"
      instructions: |
        Legacy code being incrementally migrated.
        ONLY flag: security vulnerabilities, data loss risks, crashes.
        Do NOT suggest: refactoring, naming changes, style improvements.

    - path: "scripts/**"
      instructions: |
        One-off scripts. Only flag: security issues, destructive operations
        without confirmation, missing error handling on file/network ops.

Step 4: Exclude Low-Value Files

# .coderabbit.yaml - Skip files that generate noise
reviews:
  path_filters:
    # Auto-generated files (no useful feedback possible)
    - "!**/*.lock"
    - "!**/package-lock.json"
    - "!**/pnpm-lock.yaml"
    - "!**/*.generated.*"
    - "!**/generated/**"

    # Build output
    - "!dist/**"
    - "!build/**"
    - "!**/*.min.js"
    - "!**/*.min.css"

    # Test fixtures and snapshots
    - "!**/*.snap"
    - "!**/__mocks__/**"
    - "!**/fixtures/**"
    - "!**/testdata/**"

    # Third-party code
    - "!vendor/**"
    - "!node_modules/**"

    # Data files
    - "!**/*.csv"
    - "!**/*.sql"           # DB migrations (review manually)

  auto_review:
    ignore_title_keywords:
      - "WIP"
      - "DO NOT MERGE"
      - "chore: bump"
      - "chore(deps)"
      - "auto-generated"
    drafts: false            # Skip draft PRs

Step 5: Train CodeRabbit with Learnings

# CodeRabbit learns from your feedback on PR comments.
# This improves relevance over time.

# When CodeRabbit gives feedback you disagree with, reply:
"We intentionally use default exports in this project for Next.js pages.
Please don't flag default exports in files under src/pages/."

# When CodeRabbit catches something valuable, reinforce it:
"Good catch! Always flag missing error boundaries in React components."

# View and manage learnings:
# app.coderabbit.ai > Organization > Learnings

# Learnings persist across PRs and repos within the organization.
# They are the most effective long-term tuning mechanism.

Step 6: Measure Improvement

set -euo pipefail
ORG="${1:-your-org}"
REPO="${2:-your-repo}"

echo "=== Review Quality Metrics ==="

TOTAL_PRS=0
TOTAL_COMMENTS=0

for PR_NUM in $(gh api "repos/$ORG/$REPO/pulls?state=closed&per_page=20" --jq '.[].number'); do
  COMMENTS=$(gh api "repos/$ORG/$REPO/pulls/$PR_NUM/comments" \
    --jq '[.[] | select(.user.login=="coderabbitai[bot]")] | length' 2>/dev/null || echo "0")
  if [ "$COMMENTS" -gt 0 ]; then
    TOTAL_PRS=$((TOTAL_PRS + 1))
    TOTAL_COMMENTS=$((TOTAL_COMMENTS + COMMENTS))
    echo "PR #$PR_NUM: $COMMENTS comments"
  fi
done

if [ "$TOTAL_PRS" -gt 0 ]; then
  AVG=$(( TOTAL_COMMENTS / TOTAL_PRS ))
  echo ""
  echo "Average: $AVG comments/PR"
  echo ""
  if [ "$AVG" -gt 10 ]; then
    echo "Recommendation: Switch to 'chill' profile or add path_instructions"
  elif [ "$AVG" -lt 2 ]; then
    echo "Recommendation: Switch to 'assertive' profile for more thorough reviews"
  else
    echo "Good signal-to-noise ratio"
  fi
fi

Output

• PR size guidelines documented and enforced via CI
• Review profile selected based on team needs
• Path instructions configured for relevant feedback
• Low-value files excluded from review
• Learnings trained from team feedback
• Review quality measured with metrics

Error Handling

Resources

• CodeRabbit Review Profiles
• Path Instructions Guide
• CodeRabbit Learnings

Next Steps

For learnings and advanced tuning, see coderabbit-core-workflow-b.

Source: jeremylongshore/claude-code-plugins-plus-skills → skills/.curated/coderabbit-performance-tuning/SKILL.md

Also appears in: jeremylongshore/claude-code-plugins-plus-skills/plugins/saas-packs/coderabbit-pack/skills/coderabbit-performance-tuning/SKILL.md