An advanced debugging specialist that helps diagnose and resolve code issues systematically.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondebuggerExecute the skills CLI command in your project's root directory to begin installation:
Fetches debugger from charon-fan/agent-playbook 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 debugger. Access via /debugger 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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An advanced debugging specialist that helps diagnose and resolve code issues systematically.
Activates when you:
Reproduce the issue
Gather context
# Check recent changes
git log --oneline -10
# Check error logs
tail -f logs/error.log
# Check environment
env | grep -i debug
Locate the error source
Narrow down scope
| Category | Symptoms | Investigation Steps |
|---|---|---|
| Null/Undefined | "Cannot read X of undefined" | Trace the variable origin |
| Type Errors | "X is not a function" | Check actual vs expected type |
| Async Issues | Race conditions, timing | Check promise handling, async/await |
| State Issues | Stale data, wrong state | Trace state mutations |
| Network | Timeouts, connection refused | Check endpoints, CORS, auth |
| Environment | Works locally, not in prod | Compare env vars, versions |
| Memory | Leaks, OOM | Profile memory usage |
| Concurrency | Deadlocks, race conditions | Check locks, shared state |
For each potential cause:
# Find recently modified files
find . -type f -mtime -1 -name "*.js" -o -name "*.ts" -o -name "*.py"
# Grep for error patterns
grep -r "ERROR\|FATAL\|Exception" logs/
# Search for suspicious patterns
grep -r "TODO\|FIXME\|XXX" src/
# Check for console.log left in code
grep -r "console\.log\|debugger" src/
JavaScript/TypeScript:
# Run with debug output
NODE_DEBUG=* node app.js
# Check syntax
node -c file.js
# Run tests in debug mode
npm test -- --inspect-brk
Python:
# Run with pdb
python -m pdb script.py
# Check syntax
python -m py_compile script.py
# Verbose mode
python -v script.py
Go:
# Race detection
go run -race main.go
# Debug build
go build -gcflags="-N -l"
# Profile
go test -cpuprofile=cpu.prof
# When you don't know where the bug is:
def process():
step1()
step2()
step3()
step4()
# Comment out half:
def process():
step1()
# step2()
# step3()
# step4()
# If bug disappears, uncomment half of commented:
def process():
step1()
step2()
# step3()
# step4()
# Continue until you isolate the bug
// Before (mysterious failure):
async function getUser(id: string) {
const user = await db.find(id);
return transform(user);
}
// After (with logging):
async function getUser(id: string) {
console.log('[DEBUG] getUser called with id:', id);
const user = await db.find(id);
console.log('[DEBUG] db.find returned:', user);
const result = transform(user);
console.log('[DEBUG] transform returned:', result);
return result;
}
// Complex code with bug:
function processBatch(items, options) {
// 100 lines of complex logic
}
// Create minimal reproduction:
function processBatch(items, options) {
console.log('Items:', items.length);
console.log('Options:', options);
// Test with minimal data
return processBatch([items[0]], options);
}
| Error | Likely Cause | Solution |
|---|---|---|
Cannot read property 'X' of undefined |
Accessing property on null/undefined | Add null check, use optional chaining |
X is not a function |
Wrong type, shadowing | Check typeof, verify import |
Unexpected token |
Syntax error | Check line before error, validate syntax |
Module not found |
Import path wrong | Check relative path, verify file exists |
EADDRINUSE |
Port already in use | Kill existing process, use different port |
Connection refused |
Service not running | Start service, check port |
Timeout |
Request too slow | Increase timeout, check network |
Generate a debug report:
python scripts/debug_report.py <error-message>
references/checklist.md - Debugging checklistreferences/patterns.md - Common debugging patternsreferences/errors.md - Error message referenceMake 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
β Do
β Don't
π‘ Pro Tips
β Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
β 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
pproenca/dot-skills
ailabs-393/ai-labs-claude-skills
mattpocock/skills
Solid pick for teams standardizing on skills: debugger is focused, and the summary matches what you get after install.
We added debugger from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
debugger reduced setup friction for our internal harness; good balance of opinion and flexibility.
debugger has been reliable in day-to-day use. Documentation quality is above average for community skills.
Useful defaults in debugger β fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
I recommend debugger for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
debugger reduced setup friction for our internal harness; good balance of opinion and flexibility.
Solid pick for teams standardizing on skills: debugger is focused, and the summary matches what you get after install.
I recommend debugger for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
debugger fits our agent workflows well β practical, well scoped, and easy to wire into existing repos.
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