Review Feedback Implementation

Systematically process and implement changes based on code review feedback.

When to Use

Automatically activate when the user:

• Provides reviewer comments or feedback
• Pastes PR review notes
• Mentions implementing review suggestions
• Says "address these comments" or "implement feedback"
• Shares a list of changes requested by reviewers

Systematic Workflow

1. Parse Reviewer Notes

Identify individual feedback items:

• Split numbered lists (1., 2., etc.)
• Handle bullet points or unnumbered feedback
• Extract distinct change requests
• Clarify any ambiguous items before starting

2. Create Todo List

Use TodoWrite tool to create actionable tasks:

• Each feedback item becomes one or more todos
• Break down complex feedback into smaller tasks
• Make tasks specific and measurable
• Mark first task as in_progress before starting

Example:

- Add type hints to extract function
- Fix duplicate tag detection logic
- Update docstring in chain.py
- Add unit test for edge case

3. Implement Changes Systematically

For each todo item:

Locate relevant code:

• Use Grep to search for functions/classes
• Use Glob to find files by pattern
• Read current implementation

Make changes:

• Use Edit tool for modifications
• Follow project conventions (CLAUDE.md)
• Preserve existing functionality unless changing behavior

Verify changes:

• Check syntax correctness
• Run relevant tests if applicable
• Ensure changes address reviewer's intent

Update status:

• Mark todo as completed immediately after finishing
• Move to next todo (only one in_progress at a time)

4. Handle Different Feedback Types

Code changes:

• Use Edit tool for existing code
• Follow type hint conventions (PEP 604/585)
• Maintain consistent style

New features:

• Create new files with Write tool if needed
• Add corresponding tests
• Update documentation

Documentation:

• Update docstrings following project style
• Modify markdown files as needed
• Keep explanations concise

Tests:

• Write tests as functions, not classes
• Use descriptive names
• Follow pytest conventions

Refactoring:

• Preserve functionality
• Improve code structure
• Run tests to verify no regressions

5. Validation

After implementing changes:

• Run affected tests
• Check for linting errors: uv run ruff check
• Verify changes don't break existing functionality

6. Communication

Keep user informed:

• Update todo list in real-time
• Ask for clarification on ambiguous feedback
• Report blockers or challenges
• Summarize changes at completion

Edge Cases

Conflicting feedback:

• Ask user for guidance
• Explain the conflict clearly

Breaking changes required:

• Notify user before implementing
• Discuss impact and alternatives

Tests fail after changes:

• Fix tests before marking todo complete
• Ensure all related tests pass

Referenced code doesn't exist:

• Ask user for clarification
• Verify understanding before proceeding

Important Guidelines

• Always use TodoWrite for tracking progress
• Mark todos completed immediately after each item
• Only one todo in_progress at any time
• Don't batch completions - update status in real-time
• Ask questions for unclear feedback
• Run tests if changes affect tested code
• Follow CLAUDE.md conventions for all code changes
• Use conventional commits if creating commits afterward

Example

User: "Implement these review comments:

1. Add type hints to the extract function
2. Fix the duplicate tag detection logic
3. Update the docstring in chain.py"

Actions:

1. Create TodoWrite with 3 items
2. Mark item 1 as in_progress
3. Grep for extract function
4. Read file containing function
5. Edit to add type hints
6. Mark item 1 completed
7. Mark item 2 in_progress
8. Repeat process for remaining items
9. Summarize all changes made