Verified:GitHub

systematic-debugging

Diagnose software bugs through a four-phase root cause investigation workflow.

Updated Aug 19, 2026

Search Tags:#git #debugging #troubleshooting #root cause analysis #test failures #hypothesis testing #regression testing

Ready to run

npx skills add https://github.com/jakubbartnik/honey-barrel-finale --skill systematic-debugging-jakubbartnik

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/jakubbartnik/honey-barrel-finale/tree/main/honey-barrel-finale/workspace/hermes/skills/software-development/systematic-debugging
Command: npx skills add https://github.com/jakubbartnik/honey-barrel-finale --skill systematic-debugging-jakubbartnik

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Random fixes and guess-and-check patches waste time, mask underlying issues, and introduce new bugs. This Skill enforces a disciplined debugging process that finds the root cause before any fix is attempted. ## Core Features & Use Cases - Four-Phase Process: Root cause investigation, pattern analysis, hypothesis testing, and implementation, each with explicit completion criteria. - Evidence Gathering: Guides reading error messages, reproducing failures, checking git history, and tracing data flow across components. - Rule of Three: After three failed fixes, it forces an architectural review instead of a fourth guess. - Use Case: A failing pytest suite in production gets traced from the error message through recent commits and data flow to the exact source, then fixed with a regression test proving the fix. ## Quick Start Use the systematic-debugging skill to investigate why my test suite fails and find the root cause before proposing any fix.

Frequently Asked Questions about systematic-debugging

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

FAQPage Schema

How do I debug a failing test systematically?▼

Start by reading the full error message and stack trace, then reproduce the failure consistently with a command like pytest -v. Check recent git changes, trace the data flow to the source, and only then form a hypothesis and test it with a minimal change.

What is root cause analysis in debugging?▼

Root cause analysis identifies why a bug occurs rather than patching its symptoms. It involves reproducing the issue, gathering evidence at component boundaries, and tracing bad values upstream to their origin before implementing any fix.

When should I question the architecture instead of fixing a bug?▼

After three failed fix attempts, stop and question the architecture. Signs include each fix revealing new coupling elsewhere, fixes requiring massive refactoring, or new symptoms appearing after every change.

Why do quick fixes cause more bugs?▼

Quick fixes address symptoms without understanding the underlying cause, so the real problem persists and the patch often introduces new defects. Bundling multiple changes at once also makes it impossible to isolate what actually worked.

Can systematic debugging work under time pressure?▼

Yes, systematic debugging is faster than guess-and-check thrashing even in emergencies. A structured investigation typically resolves issues in 15-30 minutes versus hours of random fix attempts with low first-time success rates.