Does this skill help with writing tests?

Phase 4 of the process requires creating a failing test case that reproduces the bug before the fix is implemented, ensuring the resolution is verified and permanent.

What is the 'Iron Law' of systematic debugging?

The Iron Law states that no fixes should be proposed or implemented until a definitive root cause has been identified through evidence-gathering and reproduction.

How does this skill handle complex multi-component systems?

It adds diagnostic instrumentation at every component boundary (such as logs for data entering and exiting an API or DB) to identify exactly which layer is failing before investigating the specific component.

What happens if a bug can't be fixed after several attempts?

If three or more fix attempts fail, the skill triggers an architectural review, questioning if the underlying pattern is fundamentally sound rather than continuing to apply symptomatic patches.

Can this skill be used for simple bugs?

Yes. While designed for complex issues, the process is fast for simple bugs and ensures that even 'quick fixes' don't mask deeper underlying issues.

Systematic Debugging

Name: Systematic Debugging
Author: dazeb

bydazeb

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보안 및 테스팅

Enforces a disciplined four-phase root cause analysis process to eliminate guess-and-check troubleshooting and recurring software bugs.

소개

The Systematic Debugging skill transforms Claude into a rigorous technical investigator by enforcing the 'Iron Law': no fixes without root cause investigation first. It guides the AI through a structured workflow—Root Cause Investigation, Pattern Analysis, Hypothesis Testing, and Implementation—to ensure bugs are fully understood before code is changed. This approach is particularly effective for complex, multi-layered systems where it uses diagnostic instrumentation to trace data flow across component boundaries, preventing the common trap of 'thrashing' where quick patches inadvertently create new issues.

주요 기능

Multi-component diagnostic instrumentation for tracing data across API, DB, and environment layers.
Automatic escalation to architectural review after three failed fix attempts.
4 GitHub stars
Evidence-based root cause investigation (Phase 1) before any code changes.
Scientific hypothesis testing to verify fixes with minimal variable changes.
Integration with Test-Driven Development (TDD) for verified bug resolution.

사용 사례

Debugging data flow issues in complex microservices or multi-layer tech stacks.
Resolving intermittent test failures or production bugs that are difficult to reproduce.
Replacing inefficient 'trial-and-error' debugging cycles with a structured, scientific methodology.

소개

주요 기능

Multi-component diagnostic instrumentation for tracing data across API, DB, and environment layers.
Automatic escalation to architectural review after three failed fix attempts.
4 GitHub stars
Evidence-based root cause investigation (Phase 1) before any code changes.
Scientific hypothesis testing to verify fixes with minimal variable changes.
Integration with Test-Driven Development (TDD) for verified bug resolution.

사용 사례

Debugging data flow issues in complex microservices or multi-layer tech stacks.
Resolving intermittent test failures or production bugs that are difficult to reproduce.
Replacing inefficient 'trial-and-error' debugging cycles with a structured, scientific methodology.