How does this skill handle complex multi-component systems?

It requires logging and verifying data at every component boundary to show exactly where data flow breaks before diving into specific component logic.

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

The Iron Law dictates that no fixes can be proposed or implemented until a thorough root cause investigation is completed. This prevents 'symptom masking' and ensures the underlying problem is solved.

Why is creating a failing test case required in Phase 4?

A failing test case provides objective proof that the bug exists and serves as a benchmark to verify that the subsequent fix actually works without causing regressions.

When should I use the Systematic Debugging skill?

Use it for any technical issue, including test failures, production bugs, performance bottlenecks, and unexpected behavior where the cause is not immediately proven by evidence.

What happens if three or more fix attempts fail?

The skill instructs you to stop and question the underlying architecture, as repeated failures often indicate a fundamental design flaw rather than a surface-level bug.

Systematic Debugging

Name: Systematic Debugging
Author: rbergman

byrbergman

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Seguridad y Pruebas

Enforces a disciplined, four-phase methodology to identify root causes and implement reliable fixes for technical issues.

The Systematic Debugging skill transforms Claude's approach to problem-solving from trial-and-error to a rigorous investigation process. By mandating a 'Root Cause First' rule, it prevents the introduction of new bugs and ensures that every fix addresses the underlying issue rather than just the symptoms. It guides the AI through evidence gathering, pattern analysis, and minimal hypothesis testing, making it an essential tool for resolving complex test failures, production bugs, and architectural instabilities where 'quick fixes' often fail.

Características Principales

011 GitHub stars

02Mandatory root cause investigation phase before any code changes

03Comparative pattern analysis against working reference implementations

04Standardized implementation workflow requiring a failing test case

05Systematic data flow tracing to isolate the source of bad values

06Minimalist hypothesis testing to isolate variables one at a time

Casos de Uso

01Resolving intermittent test failures or complex race conditions in local environments

02Diagnosing unexpected behavior in multi-component systems and microservices

03Debugging production incidents where quick patches might mask deeper regressions

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add rbergman/dark-matter-marketplace debugging

For use in Claude.ai and ChatGPT

Download Skill