systematic-debugging

Investigate root causes before fixing software bugs through a structured 4-phase process.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/Krosebrook/source-of-truth-monorepo --skill systematic-debugging-krosebrook
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/Krosebrook/source-of-truth-monorepo/tree/main/plugins/marketplaces/ai-coding-config/.claude/skills/systematic-debugging
Command: npx skills add https://github.com/Krosebrook/source-of-truth-monorepo --skill systematic-debugging-krosebrook

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prompts a systematic approach to debugging to uncover root causes before fixes.

Core Features & Use Cases

  • Evidence-first debugging: Start with error messages and reproduce reliably.
  • Traceback analysis: Map error paths to root causes.
  • Focused fixes: Apply minimal, targeted changes.

Quick Start

Use: "I have a failing test; help me reason about the root cause before proposing fixes."

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 before applying fixes?

Root-cause debugging starts by reproducing the failure reliably and gathering evidence—error messages, logs, traces—before proposing any fix. This Skill enforces a structured 4-phase process: investigate the root cause, analyze patterns across the system, form and test hypotheses, then implement targeted changes. This prevents applying surface-level patches that mask deeper issues.

What's the best way to troubleshoot a production bug across multiple components?

Systematic troubleshooting maps error paths through multi-component systems using diagnostic instrumentation and reproducible data collection. This Skill guides you through mandatory evidence gathering and structured root-cause investigation before fixes, ensuring you identify which component or interaction caused the failure rather than guessing at symptoms.

How do I investigate the root cause of a performance issue?

Root-cause investigation for performance applies the same evidence-first approach: reproduce the issue reliably, trace execution paths to identify bottlenecks, analyze patterns, form testable hypotheses, and validate before optimization. This Skill prevents premature optimization by enforcing diagnostic data collection that reveals the actual source of slowness.

Can I use traceback analysis to find integration problems between services?

Yes. Traceback analysis maps error paths across service boundaries to pinpoint integration failures. This Skill applies its 4-phase root-cause process to integration problems, requiring diagnostic instrumentation across components and structured hypothesis testing so you identify the exact interaction causing failures.

Why should I investigate root cause before fixing a test failure?

Fixing without understanding root cause often masks underlying issues—your fix may pass the test but leave the real problem unresolved. This Skill enforces evidence-first reasoning: gather error data, trace failure paths, and confirm the actual cause before implementing changes, so fixes are minimal, targeted, and address the real defect.

What anti-patterns should I avoid when debugging issues?

Common anti-patterns include skipping reproduction steps, applying fixes without evidence, ignoring multi-component context, and yielding to time pressure. This Skill explicitly resists these shortcuts by mandating diagnostic instrumentation, structured analysis phases, and reproducible data collection before any fix, ensuring thorough root-cause clarity.