Systematic workflow for verifying bug fixes to ensure quality and prevent regressions.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionbug-fixExecute the skills CLI command in your project's root directory to begin installation:
Fetches bug-fix from bobmatnyc/claude-mpm-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate bug-fix. Access via /bug-fix in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Systematic workflow for verifying bug fixes to ensure quality and prevent regressions.
Use this skill when:
Critical: Never fix a bug without first reproducing it.
## Bug Reproduction
### Steps to Reproduce
1. Navigate to `/dashboard`
2. Click "Export Data" button
3. Select date range: Jan 1 - Dec 31
4. Click "Generate Report"
### Expected Behavior
- Report downloads as CSV file
- File contains all transactions for date range
- Download completes in < 5 seconds
### Actual Behavior
- Error appears: "Failed to generate report"
- Console error: `TypeError: Cannot read property 'map' of undefined`
- No file downloads
- Issue occurs 100% of the time
### Environment
- Browser: Chrome 120.0.6099.109
- OS: macOS 14.2
- User Role: Admin
- Data Size: ~10,000 transactions
### Screenshots


Investigate WHY the bug occurs, not just WHAT happens.
## Root Cause Analysis
### Investigation
- Error occurs in `generateReport()` function at line 45
- Function assumes `transactions` array always exists
- When date range returns no results, backend returns `null`
- Frontend doesn't handle `null` case, tries to call `.map()` on `null`
### Root Cause
- Missing null check before array operations
- Backend API doesn't return consistent data structure (sometimes `[]`, sometimes `null`)
- No validation of API response shape
### Why This Wasn't Caught
- Unit tests only covered happy path (data exists)
- Integration tests didn't test empty result scenario
- Backend inconsistency not documented in API contract
Fix the root cause, not the symptom.
// BEFORE (Bug)
function generateReport(transactions) {
return transactions.map(t => ({
date: t.date,
amount: t.amount,
}));
}
// AFTER (Fixed)
function generateReport(transactions) {
// Guard against null/undefined from backend
if (!transactions || !Array.isArray(transactions)) {
console.warn('No transactions to export');
return [];
}
return transactions.map(t => ({
date: t.date,
amount: t.amount,
}));
}
Prove the bug is fixed through systematic testing.
## Fix Verification
### Testing Performed
1. ✅ Followed original reproduction steps - bug no longer occurs
2. ✅ Tested with empty date range - shows "No data to export" message
3. ✅ Tested with valid date range - exports successfully
4. ✅ Tested with large dataset (50k+ transactions) - works correctly
5. ✅ Tested in Chrome, Firefox, Safari - all working
6. ✅ Tested on staging environment - fix confirmed
### Edge Cases Tested
- Empty result set → Shows appropriate message
- Null response from API → Handled gracefully
- Single transaction → Exports correctly
- Malformed transaction data → Logs error, doesn't crash
### No New Issues
- ✅ No console errors
- ✅ No memory leaks
- ✅ No performance degradation
- ✅ Other export features still work
Critical: Every bug fix must include tests.
describe('generateReport', () => {
// Test that reproduces original bug
it('should handle null transactions gracefully', () => {
const result = generateReport(null);
expect(result).toEqual([]);
expect(console.warn).toHaveBeenCalledWith('No transactions to export');
});
// Edge cases
it('should handle undefined transactions', () => {
const result = generateReport(undefined);
expect(result).toEqual([]);
});
it('should handle empty array', () => {
const result = generateReport([]);
expect(result).toEqual([]);
});
it('should handle single transaction', () => {
const transactions = [{ date: '2025-01-01', amount: 100 }];
const result = generateReport(transactions);
expect(result).toHaveLength(1);
expect(result[0]).tMake data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
bug-fix reduced setup friction for our internal harness; good balance of opinion and flexibility.
We added bug-fix from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
bug-fix reduced setup friction for our internal harness; good balance of opinion and flexibility.
bug-fix fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
bug-fix is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
bug-fix has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in bug-fix — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend bug-fix for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Registry listing for bug-fix matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: bug-fix is focused, and the summary matches what you get after install.
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