debug-hypothesis

Name: debug-hypothesis
Availability: InStock
Author: RDCrystalLab

Structure debugging investigations with evidence-driven root cause analysis.

Updated Jan 10, 2026

Search Tags:#software engineering #debugging #root cause analysis #test failures #hypothesis testing #bug investigation #ci debugging

Ready to run

npx skills add https://github.com/RDCrystalLab/agentic-plugins --skill debug-hypothesis-rdcrystallab

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debug-hypothesis
Source: https://github.com/RDCrystalLab/agentic-plugins/tree/main/plugins/debug-hypothesis/skills/debug-hypothesis
Command: npx skills add https://github.com/RDCrystalLab/agentic-plugins --skill debug-hypothesis-rdcrystallab

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill prevents inefficient trial-and-error debugging by forcing a structured investigation process that identifies root causes before code changes are made.

Core Features & Use Cases

Scientific Debugging Loop: Guides agents through Observe → Hypothesize → Experiment → Conclude phases with evidence tracking.
Hypothesis Validation: Requires multiple possible causes, minimal experiments, and documented results in DEBUG.md before implementing fixes.
Use Case: When a test fails only in CI or a recurring bug keeps returning, use this Skill to systematically reproduce the issue, test competing explanations, and confirm the real cause.

Quick Start

Use the debug-hypothesis skill to investigate why this test fails in CI and document the root cause before applying a fix.

Frequently Asked Questions about debug-hypothesis

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

FAQPage Schema

How do I find the root cause of a failing test instead of guessing?▼

To find the root cause of a failing test, this Skill replaces guess-based fixes with structured observation and hypothesis tracking. It guides you through a scientific debugging loop to confirm causes before implementing code changes.

What is the best way to debug flaky behavior or environment differences in CI?▼

The best way to debug flaky behavior or CI environment differences is to systematically reproduce the issue and test competing explanations. This Skill enforces evidence-driven hypothesis validation and documents results in DEBUG.md.

How do I systematically investigate a software bug or regression?▼

To systematically investigate a software bug or regression, follow the Observe, Hypothesize, Experiment, and Conclude phases. This process requires minimal experiments and documented conclusions to identify root causes.

Can I use this structured debugging approach for recurring crashes?▼

Yes, you can use this structured debugging approach for recurring crashes. It applies to software debugging scenarios including regressions and flaky behavior, requiring systematic observation and documented results before applying fixes.

Why does my software bug keep returning after applying a fix?▼

Your software bug keeps returning because trial-and-error debugging often misses the actual root cause. This Skill prevents inefficient fixes by forcing evidence-driven investigation and documented conclusions in DEBUG.md before code changes.