How does this skill handle complex regressions?

It encourages tracing data flow backward from the error and comparing broken code with working reference implementations to isolate specific changes.

Does this skill help with architectural issues?

Yes, it includes specific triggers to recognize when a problem is architectural—such as when fixes require extensive refactoring or reveal new coupling issues.

Why is reproducing the issue a required step?

Reliable reproduction ensures that you aren't chasing ghosts. If you can't trigger the failure consistently, any proposed fix cannot be verified as effective.

What is the core principle of systematic debugging?

The core principle is that you are not ready to fix a bug until you can explain exactly why it is broken. It prioritizes understanding the root cause over guessing.

When should I trigger this skill in Claude Code?

Use it whenever you encounter 'not working' scenarios, error messages, test failures, or whenever you feel tempted to try a 'quick fix' without full understanding.

Systematic Debugging

Name: Systematic Debugging
Author: Light-Brands

byLight-Brands

보안 및 테스팅

Employs a rigorous, evidence-based methodology to identify root causes and implement precise fixes for software bugs and test failures.

소개

Systematic Debugging is a specialized Claude Code skill designed to eliminate guesswork by enforcing a structured investigative process. It guides the AI to analyze error messages, reproduce issues reliably, trace data flows, and validate hypotheses before attempting any code changes. By focusing on the 'why' before the 'how,' this skill ensures that fixes address the underlying problem rather than just the symptoms, making it an essential tool for resolving complex architectural issues, performance bottlenecks, and persistent test regressions.

주요 기능

Structured data flow tracing and call stack investigation
Evidence-based root cause analysis to eliminate trial-and-error fixes
Automated reproduction through pre-fix test creation
0 GitHub stars
Hypothesis testing with isolated variable changes
Recognition of architectural patterns vs. surface-level bugs

사용 사례

Debugging complex state management issues in large-scale applications
Resolving intermittent test failures in CI/CD pipelines
Investigating unexpected production behavior with no clear origin

소개

주요 기능

Structured data flow tracing and call stack investigation
Evidence-based root cause analysis to eliminate trial-and-error fixes
Automated reproduction through pre-fix test creation
0 GitHub stars
Hypothesis testing with isolated variable changes
Recognition of architectural patterns vs. surface-level bugs

사용 사례

Debugging complex state management issues in large-scale applications
Resolving intermittent test failures in CI/CD pipelines
Investigating unexpected production behavior with no clear origin