What is the 'Iron Law' of this debugging skill?

The Iron Law states that no fixes can be proposed or implemented without first completing a full root cause investigation based on observable evidence.

How does this skill handle multi-layer system failures?

It uses diagnostic instrumentation to log data at every component boundary, allowing the AI to see exactly where the data enters, transforms, or breaks between layers.

Does this skill require writing automated tests?

Yes, Phase 4 of the workflow requires creating a failing test case—either in a framework or as a standalone script—before the fix is finalized.

When should I use the debugging-patterns skill?

Use it for any technical issue, but it is especially critical for complex bugs, multi-component failures, or when 'quick fixes' have already failed to resolve the problem.

What happens if several fixes fail to work?

If three or more fix attempts fail, the skill mandates an architectural review to determine if the underlying pattern is fundamentally flawed rather than just bugged.

Systematic Debugging Patterns

Name: Systematic Debugging Patterns
Author: romiluz13

byromiluz13

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보안 및 테스팅

Enforces a rigorous, evidence-based debugging workflow to identify root causes before implementing code fixes.

소개

The Systematic Debugging Patterns skill transforms Claude Code into a disciplined bug investigator by enforcing the 'Iron Law': no code changes until a root cause is proven. It guides the AI through four distinct phases—Root Cause Investigation, Pattern Analysis, Hypothesis Testing, and Implementation—preventing common pitfalls like 'guess-and-check' fixes or symptom-masking. Especially effective for complex, multi-component systems, this skill ensures that fixes are robust, tested, and architecturally sound, significantly reducing time spent on trial-and-error debugging.

주요 기능

18 GitHub stars
Enforces a mandatory 'Root Cause First' investigation protocol.
Detects and prevents AI 'thrashing' by stopping after multiple failed fix attempts.
Requires the creation of a failing test case before any permanent fix is applied.
Implements a 'LOG FIRST' approach for evidence gathering across components.
Provides structured diagnostic patterns for tracing data flow through API and service boundaries.

사용 사례

Resolving recurring regression bugs where previous 'quick fixes' have failed to address the underlying issue.
Debugging data flow issues across multiple service boundaries or microservices.
Investigating intermittent test failures or race conditions in complex CI/CD pipelines.

소개

주요 기능

18 GitHub stars
Enforces a mandatory 'Root Cause First' investigation protocol.
Detects and prevents AI 'thrashing' by stopping after multiple failed fix attempts.
Requires the creation of a failing test case before any permanent fix is applied.
Implements a 'LOG FIRST' approach for evidence gathering across components.
Provides structured diagnostic patterns for tracing data flow through API and service boundaries.

사용 사례

Resolving recurring regression bugs where previous 'quick fixes' have failed to address the underlying issue.
Debugging data flow issues across multiple service boundaries or microservices.
Investigating intermittent test failures or race conditions in complex CI/CD pipelines.