Does this skill work with any programming language?

Yes, the methodology is language-agnostic and applies to frontend, backend, infrastructure, and configuration code alike.

What happens if multiple fix attempts fail?

If three attempts fail, the skill stops to question the underlying architecture, preventing 'thrashing' and suggesting a refactor instead of more symptom-masking patches.

Why does it require a failing test case first?

A failing test case ensures the bug is fully understood and provides an automated, objective way to verify that the fix actually works without introducing regressions.

When is the best time to use the Systematic Debugging skill?

It should be used for any technical issue, especially when under time pressure or when dealing with complex systems where the cause of an error isn't immediately obvious.

What makes this different from regular AI debugging?

It enforces a strict 'Iron Law' where no fixes can be proposed until a root cause is proven through evidence, reproduction, and diagnostic instrumentation.

Systematic Debugging Framework

Name: Systematic Debugging Framework
Author: secondsky

bysecondsky

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安全与测试

Enforces a rigorous, four-phase debugging methodology to identify root causes and ensure stable code fixes without trial-and-error thrashing.

关于

The Systematic Debugging skill provides a structured framework for Claude to resolve technical issues by enforcing a strict 'root cause first' approach. It guides the AI through deep investigation, pattern analysis, and hypothesis testing before any code changes are proposed, preventing the common pitfall of masking symptoms with quick patches. This process is particularly effective for complex systems, intermittent bugs, and high-pressure environments where manual 'guess-and-check' debugging often leads to increased technical debt and recurring failures.

主要功能

Mandatory four-phase workflow: Root Cause, Pattern Analysis, Hypothesis, and Implementation.
Diagnostic instrumentation for gathering evidence across multi-layer and distributed systems.
21 GitHub stars
Architectural sanity checks triggered automatically after three failed fix attempts.
Requirement for automated failing test cases before any fix is applied.
Evidence-based data flow tracing to locate the origin of errors within deep call stacks.

使用场景

Debugging intermittent production failures or race conditions that are hard to reproduce.
Resolving complex integration issues across multiple microservices or build layers.
Systematically fixing test suite regressions and CI/CD pipeline failures.

关于

主要功能

Mandatory four-phase workflow: Root Cause, Pattern Analysis, Hypothesis, and Implementation.
Diagnostic instrumentation for gathering evidence across multi-layer and distributed systems.
21 GitHub stars
Architectural sanity checks triggered automatically after three failed fix attempts.
Requirement for automated failing test cases before any fix is applied.
Evidence-based data flow tracing to locate the origin of errors within deep call stacks.

使用场景

Debugging intermittent production failures or race conditions that are hard to reproduce.
Resolving complex integration issues across multiple microservices or build layers.
Systematically fixing test suite regressions and CI/CD pipeline failures.