What is the 'Iron Law' of this skill?

The Iron Law is: 'No fixes without root cause investigation first.' Claude is prohibited from proposing or implementing solutions until it has gathered enough evidence to prove exactly why the failure is occurring.

How does it handle bugs that are hard to fix?

If three or more fix attempts fail, the skill triggers an architectural assessment. This forces a shift from fixing symptoms to questioning if the underlying system design is fundamentally flawed, saving hours of unproductive work.

When should I use this Claude Code skill?

Use it for any technical issue, including test failures, production bugs, or performance regressions. It is especially effective for complex multi-component systems where data flow is difficult to trace manually.

How does this skill improve my AI coding workflow?

It eliminates 'thrashing,' where an AI attempts multiple incorrect fixes. By requiring evidence and diagnostic instrumentation first, it significantly increases the first-time fix rate and prevents the introduction of new bugs.

What does the Systematic Debugging skill do?

This skill implements a rigorous four-phase investigation process—Root Cause, Pattern Analysis, Hypothesis, and Implementation—that ensures Claude identifies the actual source of a bug before attempting any code changes.

Systematic Debugging: Claude Code Skill for Root Cause Analysis

概要

Systematic Debugging provides a disciplined framework for resolving technical issues by mandating a thorough root cause investigation before any code changes are made. By following a structured process of investigation, pattern analysis, hypothesis testing, and verified implementation, this skill eliminates the 'guess-and-check' cycle that often leads to regressions and architectural thrashing. It is designed to help developers stay focused under pressure, ensuring that symptoms are not just masked, but that the underlying problems are understood and permanently resolved through evidence-based fixes and automated testing.

主な機能

Scientific hypothesis testing framework to isolate variables and verify theories minimally
Integration with test-driven development to ensure fixes are verified by failing test cases
Diagnostic instrumentation guidelines for tracing data flow across multi-component systems
10,553 GitHub stars
Architectural assessment triggers to identify when persistent bugs signal fundamental design flaws
Four-phase structured debugging methodology including root cause investigation and pattern analysis

ユースケース

Preventing the introduction of new bugs during high-pressure emergency fix scenarios
Debugging multi-layer systems where the source of an error is obscured by component boundaries
Resolving complex or intermittent test failures that resist simple troubleshooting attempts

概要

主な機能

Scientific hypothesis testing framework to isolate variables and verify theories minimally
Integration with test-driven development to ensure fixes are verified by failing test cases
Diagnostic instrumentation guidelines for tracing data flow across multi-component systems
10,553 GitHub stars
Architectural assessment triggers to identify when persistent bugs signal fundamental design flaws
Four-phase structured debugging methodology including root cause investigation and pattern analysis

ユースケース

Preventing the introduction of new bugs during high-pressure emergency fix scenarios
Debugging multi-layer systems where the source of an error is obscured by component boundaries
Resolving complex or intermittent test failures that resist simple troubleshooting attempts