review-implementing
It converts reviewer feedback into specific tasks and assists developers with implementing and verifying the requested changes.
Install
mkdir -p .claude/skills/review-implementing && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/3987" && unzip -o skill.zip -d .claude/skills/review-implementing && rm skill.zipInstalls to .claude/skills/review-implementing
Activation
This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.
Process and implement code review feedback systematically. Use when user provides reviewer comments, PR feedback, code review notes, or asks to implement suggestions from reviews.Key capabilities
- →Convert PR comments into Todo list items
- →Track implementation status of individual feedback points
- →Verify code changes against reviewer intent
- →Group related code changes into distinct execution tasks
How it works
Parses unstructured comment strings, tokenizes them into discrete tasks, and forces a sequential execution flow that ensures each item is verified before completion.
Inputs & outputs
When to use review-implementing
- →Address PR review comments
- →Manage feedback from team code reviews
- →Break down complex PR feedback into tasks
About this skill
Review Feedback Implementation
Systematically process and implement changes based on code review feedback.
When to Use
- Provides reviewer comments or feedback
- Pastes PR review notes
- Mentions implementing review suggestions
- Says "address these comments" or "implement feedback"
- Shares 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 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_progressbefore 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
completedimmediately after finishing - Move to next todo (only one
in_progressat 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 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
When not to use it
- →For high-level project management that isn't focused on code review
- →When the feedback is purely philosophical rather than actionable code changes
Limitations
- →Ambiguous reviewer language may require manual clarification
- →Requires active user oversight to define the scope of complex feedback
How it compares
It forces an interactive, iterative loop rather than attempting to apply all suggestions at once, preventing regression.
Compared to similar skills
review-implementing side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| review-implementing (this skill) | 1 | 9mo | No flags | Intermediate |
| effective-go | 323 | 9mo | No flags | Beginner |
| architect-review | 109 | 4mo | No flags | Advanced |
| resolve-conflicts | 81 | 8mo | Review | Intermediate |
Try saying
Example prompts that trigger this skill in your AI assistant.
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