test-debugging

Guide root-cause debugging for failing or flaky automated tests.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/mshuffett/dotfiles --skill test-debugging
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: test-debugging
Source: https://github.com/mshuffett/dotfiles/tree/main/agents/skills/test-debugging
Command: npx skills add https://github.com/mshuffett/dotfiles --skill test-debugging

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill addresses the frustration and time loss associated with failing or flaky automated tests by providing a structured workflow to identify and fix the root cause of the failure.

Core Features & Use Cases

  • Root Cause Analysis: Guides users through a systematic process to understand why a test is failing, not just that it failed.
  • Reproducible Failures: Encourages creating minimal reproductions to isolate the issue.
  • Meta-Learning: Integrates learnings from debugging sessions to improve future test development and maintenance.
  • Use Case: When a critical integration test begins failing intermittently, this Skill helps pinpoint whether the issue lies in the test code, the application code, or the test environment.

Quick Start

Follow the universal debugging approach outlined in this guide to analyze and fix the failing test.

Frequently Asked Questions about test-debugging

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

FAQPage Schema
What is the best way to debug a failing automated test?

A flaky test indicates an intermittent failure requiring systematic root cause analysis to determine whether the issue originates from the test code, the application code, or the test environment. Creating minimal reproductions helps isolate these inconsistent failures.

How do I find the root cause of a software test failure?

Locating the root cause of a test failure involves identifying the failure origin, creating a minimal reproduction, and determining the exact cause before applying a fix. Documenting project-specific patterns during this process improves future troubleshooting.

How do I create a minimal reproduction for a failing test?

Creating a minimal reproduction for a failing test involves systematically isolating the variables causing the error to pinpoint the exact origin. This debugging workflow ensures you understand why the test fails before implementing and verifying a fix.

Does this debugging workflow work for intermittent integration test failures?

Yes, this root cause debugging workflow is designed for failing and flaky automated tests, including intermittent integration test failures. It guides you through determining whether the failure stems from the test code, application code, or the test environment.

How does meta-learning improve software troubleshooting?

Meta-learning improves software troubleshooting by integrating learnings from debugging sessions to update root memory with universal patterns. Documenting project-specific patterns during root cause analysis refines future test development and maintenance.