systematic-debugging

Name: systematic-debugging
Availability: InStock
Author: citybear3

Guide systematic root-cause investigation before proposing fixes for technical issues.

Updated Nov 4, 2022

Search Tags:#debugging #integration #root cause analysis #hypothesis testing #incident response #guardrails #test failure

Ready to run

npx skills add https://github.com/CityBear3/dotfiles --skill systematic-debugging-citybear3

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/CityBear3/dotfiles/tree/main/claude/skills/systematic-debugging
Command: npx skills add https://github.com/CityBear3/dotfiles --skill systematic-debugging-citybear3

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It prevents wasted time and new bugs by forcing disciplined root-cause investigation before any fixes are suggested.

Core Features & Use Cases

Four-phase investigation workflow: conducts mandatory root-cause investigation, pattern analysis, hypothesis-driven testing, and then controlled implementation.
Works for any technical failure: designed for test failures, production bugs, unexpected behavior, performance issues, build failures, and integration problems.
Anti-random-fixing guardrails: explicitly blocks proposing fixes until Phase 1 is completed and limits changes so causes can be isolated.

Quick Start

Use the systematic-debugging skill to investigate an unexpected test failure by announcing the skill at the start, then completing Phase 1 before proposing any fixes.

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 a test failure before attempting fixes?▼

A systematic debugging workflow enforces a mandatory root-cause investigation phase before proposing fixes, requiring evidence gathering and reproducible data flow tracing to prevent symptom-focused guessing and isolate the actual trigger.

What is the best way to debug unexpected production bugs without causing new issues?▼

The best way to debug unexpected production bugs is using hypothesis-driven testing with single-variable changes. By applying anti-random-fixing guardrails, you limit implementation changes and verify the root cause before applying controlled fixes.

How does hypothesis testing work when investigating performance problems?▼

Hypothesis testing for performance problems works by enforcing pattern analysis and single-variable testing to isolate variables. You gather evidence, trace data flow, and confirm the root cause through controlled implementation changes rather than random adjustments.

Can I use systematic debugging for build failures and integration issues?▼

Yes, you can use systematic debugging for build failures and integration issues. The workflow applies to any technical failure by enforcing evidence gathering and reproducible tracing of data flow before allowing any implementation changes.

Why does symptom-focused guessing fail when resolving unexpected application behavior?▼

Symptom-focused guessing fails because it risks introducing new bugs and wastes time by skipping evidence gathering. A disciplined root-cause investigation requires reproducible tracing and verified hypotheses to ensure minimal implementation changes resolve the actual failure.