Debugging Methodology

Organization

DevelopersGlobal

debugging-methodology

Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.

Overview

Publisher	DevelopersGlobal
Repository	`ai-agent-skills`
Skill name	`debugging-methodology`
Stars	212
Forks	33
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by DevelopersGlobal on GitHub. Read the source before you install it.

Installation

Install the Debugging Methodology AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/DevelopersGlobal/ai-agent-skills/tree/main/skills/debugging-methodology
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/DevelopersGlobal/ai-agent-skills/tree/main/skills/debugging-methodology

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/DevelopersGlobal/ai-agent-skills.git /tmp/ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/ai-agent-skills/skills/debugging-methodology .claude/skills/debugging-methodology

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Methodology in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debugging Methodology on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debugging Methodology is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Overview

Random code changes in response to errors are not debugging — they're noise generation. This skill enforces a systematic, hypothesis-driven approach: understand the problem, form a hypothesis, test it, confirm the root cause, then fix.

AI agents often cycle through random fixes until something "works." This skill prevents that.

When to Use

Any time a test fails unexpectedly
Any time you encounter an error or exception
When behavior differs between environments
When performance degrades unexpectedly

Process

Step 1: Reproduce Reliably

Before doing anything else: reproduce the bug reliably. If you can't reproduce it, you can't fix it.
Write a failing test that captures the bug — this becomes your regression test.
Note the exact conditions that trigger the bug: inputs, environment, sequence of actions.

Verify: You can trigger the bug on demand.

Step 2: Understand Before Diagnosing

Read the full error message — not just the first line.
Read the stack trace from bottom to top — the root cause is usually near the bottom.
Identify: What was the program trying to do? What happened instead?

Verify: You can explain the bug in one sentence without using the word "error."

Step 3: Form a Hypothesis

Based on what you know, form a specific hypothesis: "I think the bug is X because Y."
The hypothesis must be falsifiable — you can design a test that proves or disproves it.
Do not start making code changes until you have a hypothesis.

Verify: Your hypothesis is specific enough to design a test for.

Step 4: Test the Hypothesis

Add targeted logging or a targeted test that confirms or refutes the hypothesis.
Run it. Read the output carefully.
If the hypothesis is wrong: update your understanding, form a new hypothesis, repeat.
If the hypothesis is right: you've found the root cause.

Verify: Root cause is confirmed by evidence, not assumed.

Step 5: Fix the Root Cause (Not the Symptom)

Fix the root cause — not the symptom. Suppressing an error message is not a fix.
Make the minimum change that fixes the root cause.
Run the failing test you wrote in Step 1 — it should now pass.
Run the full test suite — no regressions.

Verify: The specific failing test now passes. Full suite still passes.

Step 6: Prevent Recurrence

If the bug wasn't caught by existing tests: add a test that would have caught it.
If the bug was caused by a bad assumption: document the assumption or add a guard.
Consider: does this class of bug exist elsewhere in the codebase?

Common Rationalizations (and Rebuttals)

Excuse	Rebuttal
"Let me just try this and see"	Random changes produce random results. Form a hypothesis first.
"It must be a framework bug"	It's almost never the framework. Prove it before blaming it.
"Works on my machine"	Environment differences are root causes. Find them. Don't dismiss them.
"I'll add a try/catch"	That hides the bug. Find and fix the root cause.

Red Flags

Making code changes before understanding the bug
Adding try/catch to silence errors without investigating root cause
"I'll try this and see if it helps"
Assuming the bug is in a dependency before proving it
Fixing the symptom (error message) rather than the cause

Verification

Bug reproducible on demand
Root cause identified (not just symptom)
Fix targets root cause, not symptom
Reproduction test written and now passes
Full test suite passes with no regressions
Regression test added to prevent future occurrence

Frequently asked questions

What does the Debugging Methodology AI skill do?

Systematic root cause analysis for production and development bugs. Hypothesis-driven debugging — never guess-and-check.

Why use Debugging Methodology on TypingMind?

Because you install it once and use it with any model. Debugging Methodology is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debugging Methodology in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/DevelopersGlobal/ai-agent-skills/tree/main/skills/debugging-methodology. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugging Methodology?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debugging Methodology?

As many as you like. As long as a model supports skills, you can use Debugging Methodology with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debugging Methodology AI skill free?

Yes. It is published on GitHub by DevelopersGlobal under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Overview

When to Use

Process

Step 1: Reproduce Reliably

Step 2: Understand Before Diagnosing

Step 3: Form a Hypothesis

Step 4: Test the Hypothesis

Step 5: Fix the Root Cause (Not the Symptom)

Step 6: Prevent Recurrence

Common Rationalizations (and Rebuttals)

Red Flags

Verification

References

Frequently asked questions

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