What is the core principle of Systematic Debugging?

The core principle is that you should not attempt to fix a bug until you can explain exactly why it is broken. This ensures that fixes address root causes rather than just masking symptoms.

How does this skill help with intermittent bugs?

It emphasizes gathering evidence and creating reliable reproduction steps before proposing changes, preventing the 'try and see' approach that often fails with non-deterministic issues.

Why does it require a test before fixing?

Creating a reproduction test ensures the problem is fully understood and provides immediate verification that the subsequent fix actually works without breaking other functionality.

Can it identify architectural problems?

Yes, it includes specific logic to recognize when multiple failed fix attempts suggest a fundamental architectural flaw or tight coupling rather than a simple logic error.

Systematic Debugging

Name: Systematic Debugging
Author: TechNickAI

byTechNickAI

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보안 및 테스팅

Identifies the root cause of complex technical issues through evidence-based analysis and hypothesis testing before implementing fixes.

소개

The Systematic Debugging skill transforms Claude into a disciplined problem-solver that prioritizes deep understanding over rapid guessing. It provides a rigorous framework for handling test failures, build errors, and unexpected behaviors by enforcing a workflow of evidence gathering, data-flow tracing, and comparative analysis. By requiring a clear explanation of the 'why' behind a failure before proposing a solution, this skill prevents 'patch-on-patch' coding and ensures that every fix addresses the true source of the problem rather than just masking symptoms.

주요 기능

Evidence-based analysis using stack traces and error logs
Data-flow tracing to isolate the origin of corrupt values
Architectural pattern recognition for recurring or deep-seated issues
Comparative analysis between broken logic and working implementations
10 GitHub stars
Hypothesis-driven testing to validate root causes before coding

사용 사례

Resolving intermittent test failures that are difficult to reproduce
Debugging complex data flow issues across multiple system components
Fixing architectural bottlenecks where previous quick-fix attempts have failed

소개

주요 기능

Evidence-based analysis using stack traces and error logs
Data-flow tracing to isolate the origin of corrupt values
Architectural pattern recognition for recurring or deep-seated issues
Comparative analysis between broken logic and working implementations
10 GitHub stars
Hypothesis-driven testing to validate root causes before coding

사용 사례

Resolving intermittent test failures that are difficult to reproduce
Debugging complex data flow issues across multiple system components
Fixing architectural bottlenecks where previous quick-fix attempts have failed