Can I use this for production bugs?

Yes, it is specifically designed for critical issues including production bugs, performance problems, and integration failures where accuracy is vital.

Does this skill help with test creation?

Yes, the implementation phase requires creating a failing test that reproduces the bug to verify the fix works as intended.

How does this skill improve debugging speed?

It replaces time-consuming random guessing with a structured investigation, achieving a 95% success rate in 15-30 minutes by focusing on the root cause first.

Why is investigation prioritized over immediate fixes?

Immediate fixes often address symptoms rather than causes, which can lead to regression bugs and wasted developer time in the long run.

What are the four phases of the debugging skill?

The workflow includes Root Cause Investigation, Pattern Analysis, Hypothesis Testing, and Implementation/Verification.

Systematic Debugging

Name: Systematic Debugging
Author: sharpner

bysharpner

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Seguridad y Pruebas

Enforces a rigorous four-phase methodology to identify root causes and implement verified fixes for complex technical issues.

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The Systematic Debugging skill for Claude Code optimizes the troubleshooting process by mandating a root cause investigation before any code changes are attempted. It guides developers through error analysis, reproduction, hypothesis testing, and final verification using a structured framework. This approach minimizes the risk of 'band-aid' fixes and technical debt by ensuring every solution addresses the underlying source of the problem rather than just the visible symptoms, significantly increasing the success rate of complex bug resolution.

Características Principales

Hypothesis-driven testing framework
Git history and data flow analysis
Mandatory root cause investigation phase
Standardized debugging report generation
1 GitHub stars
Pattern comparison with working codebases

Casos de Uso

Resolving complex production bugs with structured reports
Identifying performance bottlenecks and integration issues
Troubleshooting intermittent test failures in CI/CD pipelines

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Características Principales

Hypothesis-driven testing framework
Git history and data flow analysis
Mandatory root cause investigation phase
Standardized debugging report generation
1 GitHub stars
Pattern comparison with working codebases

Casos de Uso

Resolving complex production bugs with structured reports
Identifying performance bottlenecks and integration issues
Troubleshooting intermittent test failures in CI/CD pipelines