How does Systematic Debugging differ from standard bug fixing?

It enforces a strict four-phase protocol that forbids any code changes until a root cause is identified and a reproducible test case is created, preventing 'guess-and-check' behavior.

What are the four phases mentioned in the skill?

The phases are Root Cause Investigation, Pattern Analysis, Hypothesis and Testing, and finally, Implementation and Verification.

When should I use this skill?

Use it for any technical issue, especially test failures, production bugs, or when a 'quick fix' has already failed to solve the underlying problem.

Does this skill work with existing test suites?

Yes, it integrates with your existing testing framework to create failing test cases that verify the bug before a fix is applied.

Can this help with performance issues?

Yes, the systematic approach works effectively for performance bottlenecks by tracing data flow and isolating the specific component causing the slowdown.

Systematic Debugging

Name: Systematic Debugging
Author: timequity

bytimequity

Security & Testing

Enforces a rigorous four-phase framework to identify root causes and verify solutions for complex software bugs and test failures.

About

The Systematic Debugging skill transforms the way Claude handles technical issues by enforcing a disciplined 'Iron Law': no fixes are attempted until a root cause is proven. By guiding the AI through investigation, pattern analysis, hypothesis testing, and rigorous implementation, this skill prevents the cycle of 'guess-and-check' coding. It is particularly valuable for resolving persistent test failures, production bugs, and subtle integration issues where quick patches would otherwise mask deeper architectural problems.

Key Features

Comprehensive data flow and multi-component boundary tracing
Four-phase framework (Root Cause, Pattern Analysis, Hypothesis, Implementation)
0 GitHub stars
Mandatory creation of failing test cases to prove bugs
Pattern comparison against known working code references
Strict 'Iron Law' enforcement to prevent premature code changes

Use Cases

Fixing 'haunted' code sections where previous quick patches have failed
Identifying root causes in complex multi-service or distributed systems
Resolving intermittent or flaky test failures in CI/CD pipelines

About

Key Features

Comprehensive data flow and multi-component boundary tracing
Four-phase framework (Root Cause, Pattern Analysis, Hypothesis, Implementation)
0 GitHub stars
Mandatory creation of failing test cases to prove bugs
Pattern comparison against known working code references
Strict 'Iron Law' enforcement to prevent premature code changes

Use Cases

Fixing 'haunted' code sections where previous quick patches have failed
Identifying root causes in complex multi-service or distributed systems
Resolving intermittent or flaky test failures in CI/CD pipelines