request-refactor-plan▌
mattpocock/skills · updated Apr 8, 2026
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Interview-driven refactoring planner that breaks changes into tiny, safe commits and files a GitHub issue.
- ›Conducts a detailed user interview to understand the problem, explore alternatives, and nail down exact scope before planning
- ›Verifies assertions by exploring the repository and assesses existing test coverage in the affected codebase
- ›Breaks implementation into the smallest possible commits, each leaving the codebase in a working state
- ›Generates a structured GitHub issue with
This skill will be invoked when the user wants to create a refactor request. You should go through the steps below. You may skip steps if you don't consider them necessary.
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Ask the user for a long, detailed description of the problem they want to solve and any potential ideas for solutions.
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Explore the repo to verify their assertions and understand the current state of the codebase.
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Ask whether they have considered other options, and present other options to them.
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Interview the user about the implementation. Be extremely detailed and thorough.
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Hammer out the exact scope of the implementation. Work out what you plan to change and what you plan not to change.
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Look in the codebase to check for test coverage of this area of the codebase. If there is insufficient test coverage, ask the user what their plans for testing are.
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Break the implementation into a plan of tiny commits. Remember Martin Fowler's advice to "make each refactoring step as small as possible, so that you can always see the program working."
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Create a GitHub issue with the refactor plan. Use the following template for the issue description:
Problem Statement
The problem that the developer is facing, from the developer's perspective.
Solution
The solution to the problem, from the developer's perspective.
Commits
A LONG, detailed implementation plan. Write the plan in plain English, breaking down the implementation into the tiniest commits possible. Each commit should leave the codebase in a working state.
Decision Document
A list of implementation decisions that were made. This can include:
- The modules that will be built/modified
- The interfaces of those modules that will be modified
- Technical clarifications from the developer
- Architectural decisions
- Schema changes
- API contracts
- Specific interactions
Do NOT include specific file paths or code snippets. They may end up being outdated very quickly.
Testing Decisions
A list of testing decisions that were made. Include:
- A description of what makes a good test (only test external behavior, not implementation details)
- Which modules will be tested
- Prior art for the tests (i.e. similar types of tests in the codebase)
Out of Scope
A description of the things that are out of scope for this refactor.
Further Notes (optional)
Any further notes about the refactor.
How to use request-refactor-plan on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add request-refactor-plan
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches request-refactor-plan from GitHub repository mattpocock/skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate request-refactor-plan. Access the skill through slash commands (e.g., /request-refactor-plan) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
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Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.7★★★★★59 reviews- ★★★★★Naina Srinivasan· Dec 28, 2024
request-refactor-plan has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Kwame Jackson· Dec 28, 2024
request-refactor-plan is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Noor Verma· Dec 28, 2024
Keeps context tight: request-refactor-plan is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Dhruvi Jain· Dec 12, 2024
request-refactor-plan is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Ama Martinez· Dec 12, 2024
Useful defaults in request-refactor-plan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Naina Anderson· Dec 4, 2024
request-refactor-plan reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Naina Singh· Nov 23, 2024
Registry listing for request-refactor-plan matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Naina White· Nov 19, 2024
Solid pick for teams standardizing on skills: request-refactor-plan is focused, and the summary matches what you get after install.
- ★★★★★Min Menon· Nov 19, 2024
We added request-refactor-plan from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Lucas Robinson· Nov 19, 2024
Useful defaults in request-refactor-plan — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
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