debug

Guide scientific-method debugging with hypothesis testing and evidence collection.

2|Updated Dec 22, 2025
One-click install
npx skills add https://github.com/TMI-apps/boilerplate-vite-supabase-mui-cursor --skill debug-tmi-apps
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: debug
Source: https://github.com/TMI-apps/boilerplate-vite-supabase-mui-cursor/tree/main/.cursor/skills/debug
Command: npx skills add https://github.com/TMI-apps/boilerplate-vite-supabase-mui-cursor --skill debug-tmi-apps

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured, science-based approach to diagnosing app and UI issues by guiding users through problem framing, evidence gathering, hypothesis generation, and iterative testing.

Core Features & Use Cases

  • Reproduces issues in a controlled, repeatable way using an explicit event chain and logs.
  • Guides nesting and event-chain analysis for React/UI problems, API calls, and UI failures.
  • Supports instrumentation, hypothesis testing, and iterative evidence collection to converge on root causes.

Quick Start

Start by reproducing the issue in the app, then collect console logs and relevant network and dashboard evidence to feed into the diagnostic workflow.

Frequently Asked Questions about debug

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

FAQPage Schema
What is a structured debugging workflow for React UI problems?

A structured debugging workflow applies the scientific method to React UI problems by framing the issue, gathering console logs, formulating hypotheses, and iteratively testing to isolate the root cause.

How do I diagnose API failures and UI glitches using a scientific method?

To diagnose API failures and UI glitches, reproduce the issue in a controlled way, collect network and dashboard evidence, then use hypothesis tracking and iterative testing to converge on the root cause.

What's the best way to find the root cause of a web app workflow glitch?

The best way to find a root cause involves reproducing the workflow glitch through an explicit event chain, instrumenting the app to collect logs, and testing hypotheses iteratively until the root cause is found.

Does this diagnostic approach work for both UI rendering issues and API failures?

Yes, this diagnostic approach works for both UI rendering issues and API failures by applying unified steps of event-chain analysis, instrumentation, and evidence collection across web apps.

Can I use this method to debug issues without adding new dependencies?

Yes, you can use this method without adding new dependencies because it encodes a structured workflow using existing console logs and network evidence rather than requiring external libraries.

Why should I use hypothesis tracking instead of trial and error for debugging?

Hypothesis tracking prevents random trial and error by enforcing evidence collection and iterative testing, ensuring each debugging step validates a specific hypothesis against the collected logs.