What happens if a proposed fix doesn't work?

The framework includes 'Red Flags' that trigger a return to Phase 1, stopping the AI from stacking multiple changes and forcing a re-evaluation of the architecture.

How does this skill handle logging and instrumentation?

It provides specific patterns for adding diagnostic logging at component boundaries and tracing data flow backward to locate the source of invalid data.

Can I use this for both frontend and backend issues?

Yes, the framework is language-agnostic and effective for UI bugs, API failures, database inconsistencies, and complex state management issues.

What makes this different from regular AI debugging?

This skill enforces a rigorous investigation-first workflow that prevents trial-and-error coding. It requires finding a verifiable root cause and creating a failing test case before any fix is attempted.

Systematic Debugging

Name: Systematic Debugging
Author: rileyhilliard

byrileyhilliard

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Seguridad y Pruebas

Implements a disciplined four-phase framework to identify and resolve the root causes of complex software bugs rather than patching symptoms.

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The Systematic Debugging skill transforms how Claude approaches troubleshooting by enforcing a rigorous, evidence-based methodology. It guides the AI through four distinct phases—Root Cause Investigation, Pattern Analysis, Hypothesis Testing, and Implementation—ensuring that engineering effort is spent on permanent solutions rather than trial-and-error fixes. By prioritizing the creation of failing test cases and verifying hypotheses with minimal changes, this skill is indispensable for tackling persistent errors, race conditions, or regressions in complex, multi-component systems.

Características Principales

62 GitHub stars
Structured four-phase debugging workflow for consistent results
Diagnostic instrumentation patterns for multi-component systems
Regression testing requirements to ensure long-term stability
Evidence-based hypothesis testing to prevent symptom-fixing
Standardized reporting format for root cause and verification

Casos de Uso

Debugging failures in distributed or multi-component architectures
Remediating recurring bugs where previous 'quick fixes' have failed
Investigating intermittent race conditions or complex logic errors

Acerca de

Características Principales

62 GitHub stars
Structured four-phase debugging workflow for consistent results
Diagnostic instrumentation patterns for multi-component systems
Regression testing requirements to ensure long-term stability
Evidence-based hypothesis testing to prevent symptom-fixing
Standardized reporting format for root cause and verification

Casos de Uso

Debugging failures in distributed or multi-component architectures
Remediating recurring bugs where previous 'quick fixes' have failed
Investigating intermittent race conditions or complex logic errors