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systematic-debugging

Convert software failures into hypothesis-driven debugging with evidence-based feedback loops.

2|Updated Apr 26, 2026
Search Tags:#test automation#debugging#root cause analysis#feedback loop#regression testing#hypothesis-driven#performance regression
One-click install
npx skills add https://github.com/iadr-dev/colab --skill systematic-debugging-iadr-dev
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: systematic-debugging
Source: https://github.com/iadr-dev/colab/tree/main/skills/systematic-debugging
Command: npx skills add https://github.com/iadr-dev/colab --skill systematic-debugging-iadr-dev

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill prevents guess-and-check debugging by forcing you to form explicit, evidence-based hypotheses before attempting any fix, reducing time spent patching symptoms and increasing root-cause clarity.

Core Features & Use Cases

  • Hypothesis-first loop: Requires 3 ordered hypotheses and a targeted test for the most likely one.
  • Deterministic feedback loops: Focuses on building a fast pass/fail signal (tests, scripts, traces, or stress loops) before changing code.
  • Evidence recording and iteration: Tracks which hypotheses are confirmed or ruled out, then iterates until the root cause is found.

Use cases include investigating failing tests, runtime errors, broken behavior, and performance regressions—especially when outcomes are unclear or intermittent.

Quick Start

Ask an agent to debug a failing test using the systematic-debugging loop by first constructing a deterministic feedback loop, reproducing the failure, writing three hypotheses, testing H1, recording evidence, and only then implementing a confidence-backed fix with a regression test.

Frequently Asked Questions about systematic-debugging

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

FAQPage Schema
How do I debug failing tests without guessing?▼

Debug failing tests by building a deterministic pass/fail feedback loop, reproducing the failure, proposing three ordered hypotheses, testing the most likely one, and recording evidence to find the root cause.

What is hypothesis-driven debugging and when should I use it?▼

Hypothesis-driven debugging converts software failures into an evidence-based workflow by forming explicit hypotheses before attempting fixes. Use it for runtime errors, broken behaviors, and performance regressions where guess-and-check is costly.

How do I find the root cause of a performance regression systematically?▼

Find the root cause of a performance regression by constructing a deterministic feedback loop, reproducing the issue, writing three ordered hypotheses, testing the most likely one, and iterating with recorded evidence until validated.

Can I use systematic debugging for intermittent runtime errors?▼

Yes, systematic debugging applies to intermittent runtime errors by requiring a deterministic pass/fail feedback loop to reproduce the failure reliably before proposing and testing hypotheses to isolate the root cause.

Why does fixing symptoms instead of root causes keep recurring?▼

Fixing symptoms recurs when you skip forming evidence-based hypotheses and fail to build a deterministic feedback loop. Systematic debugging prevents this by testing ordered hypotheses and validating fixes with regression tests.

What's the best way to stop patching symptoms and start root cause analysis?▼

Stop patching symptoms by adopting a hypothesis-driven debugging loop: build a fast pass/fail signal, write three ordered hypotheses, test the most likely, record evidence, and validate the root-cause fix with regression testing.

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