Why can't I propose a fix immediately with this skill?

The 'Iron Law' of this skill requires completing Phase 1 (Root Cause Investigation) first. Proposing fixes before understanding the root cause often leads to symptom-masking and technical debt.

What happens if three fix attempts fail consecutively?

The process triggers an architectural review. This helps you determine if the issue is a fundamental design flaw rather than a simple bug that can be patched.

Is this process too slow for simple bugs?

Actually, it is faster. Systematic debugging prevents the 'guess-and-check' thrashing that often turns a simple 5-minute fix into a 2-hour investigation.

Does this skill work with Test-Driven Development (TDD)?

Yes, Phase 4 specifically mandates creating a failing test case before implementing a fix to ensure the solution is verified and persistent.

How does it handle bugs in multi-component systems?

It implements diagnostic instrumentation at every component boundary to log data flow and environmental state, isolating the specific layer where the failure occurs.

Systematic Debugging

Name: Systematic Debugging
Author: pur3v4d3r

bypur3v4d3r

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安全与测试

Implements a disciplined, root-cause-first framework for software debugging to eliminate guesswork and prevent regression.

关于

This skill provides a rigorous four-phase framework for identifying and resolving technical issues by mandating exhaustive root-cause investigation before any code changes are proposed. It guides users through consistent reproduction, multi-component diagnostic instrumentation, and minimal hypothesis testing, ensuring that fixes address the underlying problem rather than masking symptoms. By enforcing the 'Iron Law' of no fixes without evidence, it significantly improves first-time fix rates and prevents the introduction of new bugs in complex systems.

主要功能

Scientific hypothesis testing with minimal variable changes
1 GitHub stars
Multi-component diagnostic instrumentation for complex system boundaries
Backward data flow tracing and pattern-based difference analysis
Mandatory root-cause identification before proposing any code changes
Automated architectural review triggers after three failed fix attempts

使用场景

Debugging complex test failures and regressions that lack clear stack traces
Resolving intermittent production bugs in distributed or multi-layered architectures
Investigating deep-seated performance bottlenecks and build pipeline failures

关于

主要功能

Scientific hypothesis testing with minimal variable changes
1 GitHub stars
Multi-component diagnostic instrumentation for complex system boundaries
Backward data flow tracing and pattern-based difference analysis
Mandatory root-cause identification before proposing any code changes
Automated architectural review triggers after three failed fix attempts

使用场景

Debugging complex test failures and regressions that lack clear stack traces
Resolving intermittent production bugs in distributed or multi-layered architectures
Investigating deep-seated performance bottlenecks and build pipeline failures