debug-like-expert

Diagnose complex issues through hypothesis testing and controlled experiments.

2|1|Updated Oct 8, 2009

Search Tags:#debugging #investigation #verification #observability #hypothesis #reproducibility #root-cause

Ready to run

npx skills add https://github.com/riethmayer/dotfiles --skill debug-like-expert-riethmayer

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: debug-like-expert
Source: https://github.com/riethmayer/dotfiles/tree/main/stow/agents/.agents/skills/debug-like-expert
Command: npx skills add https://github.com/riethmayer/dotfiles --skill debug-like-expert-riethmayer

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill provides a disciplined, evidence-led approach to debugging, replacing guesswork with a repeatable framework that identifies root causes using data, experiments, and verification.

Core Features & Use Cases

Hypothesis-driven reasoning: Form specific, testable hypotheses and prioritize them by evidence strength.
Evidence gathering: Collect logs, traces, and artifacts before proposing fixes to ensure verifiable conclusions.
Structured verification: Use minimal reproductions, controlled experiments, and cross-environment checks to confirm fixes.
Use Case: When facing stubborn bugs that resist quick fixes, apply this Skill to map potential causes, test them one-at-a-time, and validate outcomes with repeatable results.

Quick Start

Start by describing the bug and then formulate 3–5 testable hypotheses, design one-at-a-time experiments, and verify results with concrete evidence.

Frequently Asked Questions about debug-like-expert

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

FAQPage Schema

How do I find a root cause when quick fixes fail during debugging?▼

To find a root cause when quick fixes fail, apply evidence-led debugging by formulating 3–5 testable hypotheses, designing controlled experiments one at a time, and verifying results with concrete data like logs and traces.

What is a hypothesis-driven debugging workflow for complex software issues?▼

A hypothesis-driven debugging workflow replaces guesswork by requiring developers to form specific, testable hypotheses, prioritize them by evidence strength, and validate outcomes through minimal reproductions and cross-environment checks.

How do I verify a bug fix using controlled experiments and reproducibility?▼

To verify a bug fix using reproducibility, create minimal reproductions, run controlled experiments that isolate variables, and cross-check outcomes across environments to ensure the fix resolves the issue without side effects.

When do I need structured observability and evidence gathering for bug investigation?▼

You need structured observability and evidence gathering when facing stubborn bugs that resist quick fixes, requiring you to collect logs, traces, and artifacts before proposing fixes to ensure verifiable conclusions.

What is the best way to debug complex issues without relying on guesswork?▼

The best way to debug complex issues without guesswork is mapping potential causes, testing them one at a time through controlled experiments, and validating outcomes with documented hypotheses and repeatable evidence.

Can I use this debugging approach for bugs that require cross-environment verification?▼

Yes, this approach applies to bugs requiring cross-environment verification by using controlled experiments and structured observability to confirm fixes across different environments with documented, repeatable results.