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systematic-debugging

Prioritize fragile code segments using historical bug-proneness metrics.

11|3|Updated Jan 29, 2026
Search Tags:#debugging#mcp#code-analysis#root-cause-analysis#git-history#bug-hunting
One-click install
npx skills add https://github.com/artk0de/TeaRAGs-MCP --skill systematic-debugging-artk0de
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/artk0de/TeaRAGs-MCP/tree/main/.claude-plugin/dinopowers/skills/systematic-debugging
Command: npx skills add https://github.com/artk0de/TeaRAGs-MCP --skill systematic-debugging-artk0de

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the issue of inefficient debugging by replacing blind code searches with a data-driven approach that prioritizes code segments historically prone to bugs, churn, and instability.

Core Features & Use Cases

  • Bug-Hunt Prioritization: Automatically ranks code suspects based on bugFixRate and churn metrics before investigation begins.
  • Causal Chain Tracing: Maps the execution path from entry points to suspected faults, danger-ranking each hop to identify the most likely failure source.
  • Use Case: When a user reports a specific stack trace or error, this skill identifies the most fragile modules in the codebase and guides the agent to investigate those areas first, preventing the agent from wasting time on stable, healthy code.

Quick Start

Invoke the systematic-debugging skill by providing the error message or stack trace from your failing test or application.

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 stack trace by prioritizing historically fragile code?▼

To debug using historical bug-proneness data, you provide the error message or stack trace from your failing test or application, and the system ranks code suspects based on bugFixRate and churn metrics to guide investigation.

What is causal chain tracing for root cause analysis?▼

Causal chain tracing for root cause analysis maps the execution path from entry points to suspected faults, danger-ranking each hop to identify the most likely failure source during software maintenance and regression hunting.

How do I identify bug-prone modules in a codebase for regression hunting?▼

Identifying bug-prone modules for regression hunting requires integrating bug-hunt metrics to automatically rank code suspects by historical instability and churn, guiding the agent to investigate the most fragile areas first.

Do I need bug-hunt metrics to use data-driven code analysis?▼

Yes, data-driven code analysis requires integration with bug-hunt metrics and systematic-debugging superpowers to perform ranked hypothesis testing and prioritize investigation of fragile code segments.

What is the best way to resolve stack traces without blindly searching code?▼

The best way to resolve stack traces without blind searches is applying a data-driven approach that uses historical bug-proneness data to identify the most fragile modules and guide investigation there first.

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