debugging-systematic

Diagnose bugs and test failures using a four-phase root cause analysis framework.

3|Updated Aug 26, 2026
One-click install
npx skills add https://github.com/Fabric-Pro/fabric-oss --skill debugging-systematic-fabric-pro
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: debugging-systematic
Source: https://github.com/Fabric-Pro/fabric-oss/tree/main/.cursor/skills/debugging-systematic
Command: npx skills add https://github.com/Fabric-Pro/fabric-oss --skill debugging-systematic-fabric-pro

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Developers often waste hours applying guesswork fixes that mask symptoms instead of resolving the underlying defect. This Skill enforces a systematic, evidence-driven debugging methodology so bugs, flaky tests, race conditions, and production issues are fixed at their root cause the first time. ## Core Features & Use Cases - Four-Phase Framework: Structured workflow covering root cause investigation, pattern analysis, hypothesis testing, and verified implementation. - Debugging Techniques Library: Practical guidance on git bisect, differential debugging, instrumentation, rubber duck debugging, and time-travel debugging. - Common Root Cause Catalog: Concrete code examples for race conditions, shared mutable state, incorrect assumptions, off-by-one errors, and timezone issues. - Use Case: When a login endpoint fails intermittently, use this Skill to reproduce the failure, gather log evidence, form a testable hypothesis (e.g., clock skew between servers), and verify the fix against the reproducer before shipping. ## Quick Start Ask the AI to systematically debug the failing test or production error you are seeing, starting with root cause investigation before proposing any fix.

Frequently Asked Questions about debugging-systematic

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

FAQPage Schema
How do I find the root cause of a bug systematically?▼

Follow a four-phase process: reproduce the issue reliably, gather evidence like full stack traces and logs, form a specific testable hypothesis, then design experiments that prove or disprove it. Only implement a fix after the root cause is confirmed with evidence.

How to debug intermittent or flaky test failures?▼

Intermittent failures usually indicate race conditions, shared mutable state, or timing dependencies. Isolate the failing component, add timestamped instrumentation around async operations, and check whether tests share global state that leaks between runs.

How do I use git bisect to find a breaking commit?▼

Run git bisect start, mark the current broken commit with git bisect bad, and mark a known working commit with git bisect good. Git checks out intermediate commits for you to test, binary-searching until it identifies the exact commit that introduced the regression.

Why does my code work in the debugger but fail in production?▼

This pattern typically signals a race condition or timing issue, since debuggers change execution timing. Look for async operations completing in unexpected order, missing awaits, or shared state accessed concurrently, then add logging with timestamps to confirm.

When should I ask for help instead of debugging alone?▼

Ask for help after roughly two hours of systematic investigation, when the issue involves an unfamiliar subsystem, or when the reproducer is inconsistent. Prepare a written summary with reproduction steps, evidence gathered, hypotheses ruled out, and environment details first.