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/debug-error

Systematically debug and fix errors

From plugin
claude-command-suite
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$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-code

Command definition

debug-error.md

Systematically Debug and Fix Errors

Systematically debug and fix errors

Instructions

Follow this comprehensive debugging methodology to resolve: **$ARGUMENTS**

1. **Error Information Gathering**

  • Collect the complete error message, stack trace, and error code
  • Note when the error occurs (timing, conditions, frequency)
  • Identify the environment where the error happens (dev, staging, prod)
  • Gather relevant logs from before and after the error

2. **Reproduce the Error**

  • Create a minimal test case that reproduces the error consistently
  • Document the exact steps needed to trigger the error
  • Test in different environments if possible
  • Note any patterns or conditions that affect error occurrence

3. **Stack Trace Analysis**

  • Read the stack trace from bottom to top to understand the call chain
  • Identify the exact line where the error originates
  • Trace the execution path leading to the error
  • Look for any obvious issues in the failing code

4. **Code Context Investigation**

  • Examine the code around the error location
  • Check recent changes that might have introduced the bug
  • Review variable values and state at the time of error
  • Analyze function parameters and return values

5. **Hypothesis Formation**

  • Based on evidence, form hypotheses about the root cause
  • Consider common causes:
  • Null pointer/undefined reference
  • Type mismatches
  • Race conditions
  • Resource exhaustion
  • Logic errors
  • External dependency failures

6. **Debugging Tools Setup**

  • Set up appropriate debugging tools for the technology stack
  • Use debugger, profiler, or logging as needed
  • Configure breakpoints at strategic locations
  • Set up monitoring and alerting if not already present

7. **Systematic Investigation**

  • Test each hypothesis methodically
  • Use binary search approach to isolate the problem
  • Add strategic logging or print statements
  • Check data flow and transformations step by step

8. **Data Validation**

  • Verify input data format and validity
  • Check for edge cases and boundary conditions
  • Validate assumptions about data state
  • Test with different data sets to isolate patterns

9. **Dependency Analysis**

  • Check external dependencies and their versions
  • Verify network connectivity and API availability
  • Review configuration files and environment variables
  • Test database connections and query execution

10. **Memory and Resource Analysis**

  • Check for memory leaks or excessive memory usage
  • Monitor CPU and I/O resource consumption
  • Analyze garbage collection patterns if applicable
  • Check for resource deadlocks or contention

11. **Concurrency Issues Investigation**

  • Look for race conditions in multi-threaded code
  • Check synchronization mechanisms and locks
  • Analyze async operations and promise handling
  • Test under different load conditions

12. **Root Cause Identification**

  • Once the cause is identified, understand why it happened
  • Determine if it's a logic error, design flaw, or external issue
  • Assess the scope and impact of the problem
  • Consider if similar issues exist elsewhere

13. **Solution Implementation**

  • Design a fix that addresses the root cause
  • Consider multiple solution approaches and trade-offs
  • Implement the fix with appropriate error handling
  • Add validation and defensive programming where needed

14. **Testing the Fix**

  • Test the fix against the original error case
  • Test edge cases and related scenarios
  • Run regression tests to ensure no new issues
  • Test under various load and stress conditions

15. **Prevention Measures**

  • Add appropriate unit and integration tests
  • Improve error handling and logging
  • Add input validation and defensive checks
  • Update documentation and code comments

16. **Monitoring and Alerting**

  • Set up monitoring for similar issues
  • Add metrics and health checks
  • Configure alerts for error thresholds
  • Implement better observability

17. **Documentation**

  • Document the error, investigation process, and solution
  • Update troubleshooting guides
  • Share learnings with the team
  • Update code comments with context

18. **Post-Resolution Review**

  • Analyze why the error wasn't caught earlier
  • Review development and testing processes
  • Consider improvements to prevent similar issues
  • Update coding standards or guidelines if needed

Remember to maintain detailed notes throughout the debugging process and consider the wider implications of both the error and the fix.

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A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.

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