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systematic-debugging

Investigate bugs and test failures systematically before applying fixes.

641|104|Updated Mar 5, 2026
Search Tags:#debugging#troubleshooting#evidence#root-cause#systems-analysis#bug-triage#test-failure
One-click install
npx skills add https://github.com/loongclaw-ai/loongclaw --skill systematic-debugging-loongclaw-ai
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/loongclaw-ai/loongclaw/tree/main/skills/systematic-debugging
Command: npx skills add https://github.com/loongclaw-ai/loongclaw --skill systematic-debugging-loongclaw-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This process forces developers to stop guessing and fully understand bugs, test failures, or unexpected behaviors before attempting any fixes to avoid wasting time and introducing new regressions.

Core Features & Use Cases

  • Four-phase workflow: Phase 1 investigates errors, reproduces issues, checks recent changes, and traces data flow before any fix.
  • Pattern analysis and hypothesis testing: Phase 2 compares with working code, while Phase 3 forms minimal hypotheses and verifies them one change at a time.
  • Rigorous implementation guardrails: Phase 4 ensures failing tests exist, fixes target root causes, and prompts architectural review after repeated failures, such as debugging a flaky CI integration test by logging each service boundary before changing code.

Quick Start

Use the systematic-debugging skill to investigate failing tests by carefully reading the error, reproducing it, and gathering evidence before suggesting any code changes.

Frequently Asked Questions about systematic-debugging

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

FAQPage Schema
What is the best way to find the root cause of a flaky CI integration test failure?▼

Root cause analysis for flaky CI test failures requires systematically logging each service boundary and tracing data flow before changing code. This disciplined approach documents evidence to avoid guessing and prevents introducing regressions.

How do I debug test failures and unexpected behaviors without guessing?▼

Debug test failures by following a four-phase workflow: investigate errors, reproduce issues, check recent changes, and trace data flow. Form minimal hypotheses and verify them one change at a time to ensure reliable debugging.

Why does fixing a bug without evidence often introduce new regressions?▼

Fixing bugs without evidence introduces regressions because developers guess instead of understanding the root cause. Documenting evidence and comparing with working code ensures fixes target the actual problem rather than masking symptoms.

Can I use systematic troubleshooting for multi-component systems and developer workflows?▼

Yes, systematic troubleshooting applies to multi-component systems, CI pipelines, and developer workflows. It demands traceable root cause analysis across data flows to reliably diagnose unexpected behaviors before proposing fixes.

What should I do when repeated bug fixes keep failing in my codebase?▼

When repeated bug fixes fail, prompt an architectural review. Ensure failing tests exist before applying fixes, verify hypotheses one change at a time, and compare against working code to isolate the true root cause.

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