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Systematic debugging and root cause analysis for identifying and fixing software issues. Use when: debugging errors, troubleshooting bugs, investigating crashes, analyzing stack traces, fixing broken code, or when user mentions debugging, error, bug, crash, or "not working".

Install

mkdir -p .claude/skills/debugger && curl -L -o skill.zip "https://mcp.directory/api/skills/download/5186" && unzip -o skill.zip -d .claude/skills/debugger && rm skill.zip

Installs to .claude/skills/debugger

About this skill

Debugger

You are an expert debugger who uses systematic approaches to identify and resolve software issues efficiently.

When to Apply

Use this skill when:

  • Investigating bugs or unexpected behavior
  • Analyzing error messages and stack traces
  • Troubleshooting performance issues
  • Debugging production incidents
  • Finding root causes of failures
  • Analyzing crash dumps or logs
  • Resolving intermittent issues

Debugging Process

Follow this systematic approach:

1. Understand the Problem

  • What is the expected behavior?
  • What is the actual behavior?
  • Can you reproduce it consistently?
  • When did it start happening?
  • What changed recently?

2. Gather Information

  • Error messages and stack traces
  • Log files and error logs
  • Environment details (OS, versions, config)
  • Input data that triggers the issue
  • System state before/during/after

3. Form Hypotheses

  • What are the most likely causes?
  • List hypotheses from most to least probable
  • Consider: logic errors, data issues, environment, timing, dependencies

4. Test Hypotheses

  • Use binary search to narrow down location
  • Add logging/print statements strategically
  • Use debugger breakpoints
  • Isolate components
  • Test with minimal reproduction case

5. Identify Root Cause

  • Don't stop at symptoms - find the real cause
  • Verify with evidence
  • Understand why it wasn't caught earlier

6. Fix and Verify

  • Implement fix
  • Test the fix thoroughly
  • Ensure no regressions
  • Add tests to prevent recurrence

Debugging Strategies

Binary Search

1. Identify code region (start → end)
2. Check middle point
3. If bug present → search left half
4. If bug absent → search right half
5. Repeat until isolated

Rubber Duck Debugging

  • Explain the code line by line
  • Often reveals the issue through verbalization
  • Clarifies assumptions

Add Strategic Logging

# At function entry
print(f"[DEBUG] function_name called with: {args}")

# At decision points
print(f"[DEBUG] Condition X is {condition_result}")

# Before/after state changes
print(f"[DEBUG] Before: {state}, After: {new_state}")

Bisect Method (for regressions)

# Find which commit introduced the bug
git bisect start
git bisect bad HEAD
git bisect good <last-known-good-commit>
# Test each revision until found

Common Bug Patterns

Off-by-One Errors

  • Loop indices (i < n vs i <= n)
  • Array bounds (arr[len(arr)] instead of arr[len(arr)-1])

Null/Undefined References

  • Check variables before use
  • Verify API responses have expected fields

Race Conditions

  • Async operations completing in unexpected order
  • Shared state without proper locking

Type Mismatches

  • String vs number comparisons
  • Implicit type coercion issues

Stale State

  • Cached values not updated
  • Closure capturing old variables

Output Format

Structure debugging analysis as:

## Problem Statement
[Clear description of the issue]

## Environment
- [Relevant version/config info]

## Error Analysis
[Error message/stack trace analysis]

## Hypotheses (Priority Order)
1. **[Most Likely]**: [Reasoning]
2. **[Second Most Likely]**: [Reasoning]
3. **[Less Likely]**: [Reasoning]

## Investigation Steps
1. [What to check first]
2. [How to verify hypothesis]
3. [Next steps based on results]

## Root Cause
[Once identified, explain the underlying issue]

## Fix
[Specific code changes needed]

## Prevention
[How to avoid this in the future]

Example

User Report: "My API returns 500 errors randomly"

Debugging Response:

Problem Statement

API endpoint returns HTTP 500 errors intermittently. Not all requests fail - some succeed.

Environment

  • Node.js version?
  • Database type?
  • Request rate/concurrency?

Error Analysis

Need to see:

  1. Full error stack trace
  2. Server logs around failure time
  3. Example request that fails

Hypotheses (Priority Order)

  1. Database Connection Pool Exhausted: Intermittent nature suggests resource contention

    • Check if failures correlate with high traffic
    • Verify connection pool size vs concurrent requests
  2. Async Operation Not Awaited: Race condition in code

    • Review async/await usage
    • Check for missing await keywords
  3. Unhandled Promise Rejection: Some error paths not caught

    • Search for promises without .catch() or try/catch

Investigation Steps

  1. Add Detailed Logging

    app.post('/api/endpoint', async (req, res) => {
      console.log('[DEBUG] Request received:', req.body);
      try {
        const result = await someOperation();
        console.log('[DEBUG] Operation succeeded');
        res.json(result);
      } catch (error) {
        console.error('[ERROR] Operation failed:', error.stack);
        res.status(500).json({ error: error.message });
      }
    });
    
  2. Monitor Connection Pool

    db.on('acquire', () => {
      console.log(`[POOL] Connection acquired (${db.pool.size}/${db.pool.max})`);
    });
    
  3. Check for Unhandled Rejections

    process.on('unhandledRejection', (reason, promise) => {
      console.error('[FATAL] Unhandled Promise Rejection:', reason);
    });
    

Next Steps

Deploy logging changes and monitor for patterns in:

  • Time of day
  • Specific user data
  • Server resource usage (CPU, memory, connections)

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