microboxlabs/coderabbit-pr-review

CodeRabbitAI PR Review and Fix

Review the current branch's PR for CodeRabbitAI inline comments and fix all valid issues.

Prerequisites

• gh CLI must be authenticated (gh auth status). No additional tokens required.

Workflow

1. Fetch CodeRabbitAI comments

Auto-detect the PR number, then fetch all inline review comments from the bot:

PR_NUMBER=$(gh pr view --json number -q '.number')

gh api "repos/{owner}/{repo}/pulls/${PR_NUMBER}/comments" \
  --paginate \
  --jq '[.[] | select(.user.login == "coderabbitai[bot]") | {id, path, line, start_line, body, diff_hunk, created_at, pull_request_review_id}]'

If gh pr view fails (no PR for this branch), ask the user for the PR number or use -p <number>.

If no comments are returned, report "No CodeRabbitAI comments found on this PR" and stop.

2. Parse each comment

Each comment body follows a structured format. Extract these fields:

| Field | How to extract | |-------|---------------| | Severity | First line: _🟠 Major_ or _🟡 Minor_ | | Title | First **...** bold text after severity | | Explanation | Text between title and first <details> block | | File | path field from API response | | Line | line field (end line); start_line if range | | Suggested fix | Content inside <details><summary>Suggested fix</summary> | | AI agent prompt | Content inside <details><summary>🤖 Prompt for AI Agents</summary> |

The AI agent prompt is specifically designed for LLM consumption and is the primary guide for fixes.

For parsing details, see reference.md.

3. Group by file and plan fixes

• Group parsed comments by File path.
• Within each file, order by Line number (ascending) so edits applied top-to-bottom keep line numbers valid.
• Read each referenced file to prepare for validation.

4. Validate and apply fixes

For each comment, in file-then-line order:

1. Read the actual code at the referenced file and line range.
2. Evaluate validity:
   • Is the code actually doing what CodeRabbit claims?
   • Does the suggestion align with existing project patterns?
   • Is this a meaningful improvement or just a style preference?
   • Would the fix introduce regressions?
   • Has the code already been changed since the comment was posted?
3. If valid: Apply the fix using the AI agent prompt as primary guide, cross-referencing the suggested fix diff. Make minimal changes. Preserve existing style.
4. If invalid: Skip it. Record the comment title and a one-line reason (e.g., "CodeRabbit misread context", "style preference conflicting with project convention", "code already fixed").

After editing a file, re-read affected regions to ensure no new issues (broken imports, syntax errors).

5. Verify

• Type-check changed files: npx tsc --noEmit
• Run tests for affected modules if available (e.g., npx vitest run <path>)

6. Report summary

After all comments are processed, report:

• Fixed (count): Comment title and file, one line each.
• Skipped (count): Comment title, file, and one-line reason.
• Reminder to review changes and run the full test suite.

Severity guidance

| Marker | Level | Action | |--------|-------|--------| | 🟠 Major | Likely bug, security, or correctness issue | Prioritize fixing | | 🟡 Minor | Code smell, missing edge case, improvement | Validate carefully — higher false-positive rate |

Important guidelines

• Never blindly apply all suggestions. Always validate against the actual code.
• Prefer the AI agent prompt over the suggested diff when they conflict.
• One logical edit per fix. Do not combine multiple fixes into a single edit.
• Preserve project style. Do not change formatting or patterns beyond what the fix requires.
• Watch for stale comments. If the code has changed since the comment, skip with a note.

For the full comment schema and edge cases, see reference.md.