systematic-debugging

Trace software failures to root causes before proposing fixes.

1|Updated Apr 27, 2026

Search Tags:#testing #debugging #root cause #troubleshooting #diagnostics #flaky tests

Ready to run

npx skills add https://github.com/AIChuY/ai_workflow --skill systematic-debugging-aichuy

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/AIChuY/ai_workflow/tree/main/systematic-debugging
Command: npx skills add https://github.com/AIChuY/ai_workflow --skill systematic-debugging-aichuy

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It helps engineers stop guessing and find the real cause of bugs, flaky tests, and unexpected behavior before changing code.

Core Features & Use Cases

Read errors carefully, reproduce issues, and trace data flow back to the original trigger instead of patching symptoms.
Compare broken behavior with working examples, gather diagnostics across component boundaries, and test one hypothesis at a time.
Use it for build failures, production bugs, integration issues, and test flakiness where quick fixes tend to backfire.

Quick Start

Use the systematic-debugging skill to investigate this failure from the first error message through root cause, then propose the smallest verified fix.

Frequently Asked Questions about systematic-debugging

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I find the root cause of flaky tests instead of patching symptoms?▼

To find the root cause of flaky tests, you must read errors carefully, reproduce the issue, and trace data flow back to the original trigger. This approach compares broken behavior with working examples to test minimal hypotheses before proposing a verified fix.

What is the best way to debug multi-component failures across system boundaries?▼

The best way to debug multi-component failures is evidence-driven investigation. You gather diagnostics across component boundaries, compare broken behavior with working examples, and test one hypothesis at a time to isolate the original trigger before changing code.

How do I troubleshoot production bugs and performance regressions systematically?▼

To troubleshoot production bugs and performance regressions systematically, start by reading the first error message, gather reproducible evidence, and trace symptoms back to the root cause. This prevents quick fixes from backfiring in complex systems.

When should I use a systematic debugging approach for build breaks?▼

You should use systematic debugging for build breaks when quick fixes tend to backfire and the failure spans multiple components. It requires careful error reading and minimal hypothesis testing to drive an evidence-driven investigation before proposing a fix.

Why do my quick fixes for unexpected software behavior keep backfiring?▼

Quick fixes for unexpected behavior backfire because they patch symptoms rather than addressing the root cause. By skipping evidence-driven investigation and reproducible data tracing, you fail to isolate the original trigger, leaving the underlying defect intact.