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Structured Debug

CommunityPopular
codeaholicguy
structured-debug

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

Overview

Publishercodeaholicguy
Repositoryai-devkit
Skill namestructured-debug
Stars
1.6K
Forks
252
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by codeaholicguy on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/codeaholicguy/ai-devkit.git /tmp/ai-devkit
mkdir -p .claude/skills
cp -r /tmp/ai-devkit/skills/structured-debug .claude/skills/structured-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Structured Debug 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 Structured Debug 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 Structured Debug 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.

Local Debugging Assistant

Debug with an evidence-first workflow before changing code.

Hard Rule

  • Do not modify code until the user approves a selected fix plan.

Workflow

  1. Clarify
  • Restate observed vs expected behavior in one concise diff.
  • Confirm scope and measurable success criteria.
  • Before investigating, search for similar past incidents: npx ai-devkit@latest memory search --query "<observed behavior>" --tags "debug,root-cause"
  1. Reproduce
  • Capture minimal reproduction steps.
  • Capture environment fingerprint: runtime, versions, config flags, data sample, and platform.
  1. Hypothesize and Test For each hypothesis, include:
  • Predicted evidence if true.
  • Disconfirming evidence if false.
  • Exact test command or check.
  • Prefer one-variable-at-a-time tests.
  1. Plan
  • Present fix options with risks and verification steps.
  • Recommend one option and request approval.

Validation

  • Confirm a pre-fix failing signal exists.
  • Confirm post-fix success using the verify skill — including regression verification for bug fixes.
  • Summarize remaining risks and follow-ups.
  • Store root cause and fix for future sessions: npx ai-devkit@latest memory store --title "<root cause>" --content "<diagnosis and fix>" --tags "debug,root-cause"

Task Tracing

If task tracing is usable, choose a short kebab-case debug task name when no task name exists, then use task optionally: record repro/final results as evidence, the current hypothesis as next, and blockers only when they materially affect progress. Never block debugging because task tracing is unavailable.

Red Flags and Rationalizations

RationalizationWhy It's WrongDo Instead
"I already know the cause"Assumptions skip evidenceReproduce and prove it first
"This is urgent, just fix it"A wrong fix wastes more time10 minutes of diagnosis saves hours
"The fix is obvious from the stack trace"Stack traces show symptoms, not causesTrace backward to the root cause

Output Template

Use this response structure:

  • Observed vs Expected
  • Repro and Environment
  • Hypotheses and Tests
  • Options and Recommendation
  • Validation Plan and Results
  • Open Questions

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Structured Debug AI skill do?

AI DevKit · Guide structured debugging before code changes by clarifying expected behavior, reproducing issues, identifying likely root causes, and agreeing on a fix plan with validation steps. Use when users ask to debug bugs, investigate regressions, triage incidents, diagnose failing behavior, handle failing tests, analyze production incidents, investigate error spikes, or run root cause analysis (RCA).

Why use Structured Debug on TypingMind?

Because you install it once and use it with any model. Structured Debug 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 Structured Debug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/codeaholicguy/ai-devkit/tree/main/skills/structured-debug. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Structured Debug?

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 Structured Debug?

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

Is the Structured Debug AI skill free?

Yes. It is published on GitHub by codeaholicguy under the Apache-2.0 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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