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Debugging Workflow

Community
thienanblog
debugging-workflow

Reproduce, isolate, and fix unexplained failures, regressions, or flaky behavior. Use when the cause is uncertain; a known-cause fix usually needs only the normal implementation workflow.

Overview

Publisherthienanblog
Repositoryawesome-ai-agent-skills
Skill namedebugging-workflow
Stars
66
Forks
21
Bundled files
2
LicenseApache-2.0
Links
Skill sourceRepository
  • 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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Debugging Workflow 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/thienanblog/awesome-ai-agent-skills.git /tmp/awesome-ai-agent-skills
mkdir -p .claude/skills
cp -r /tmp/awesome-ai-agent-skills/plugins/project-development-skills/skills/debugging-workflow .claude/skills/debugging-workflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugging Workflow 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 Workflow 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 Workflow 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.

Debugging Workflow

Use evidence to locate the failing boundary and fix the cause while preserving business intent.

Working agreement

Follow the user's request and applicable repository instructions over these defaults. Use existing authorization; ask only about missing decisions that materially affect scope, cost, safety, or the result. Continue independent authorized work while awaiting an answer.

Run in the main conversation by default. Delegation can increase usage: obtain explicit approval for the proposed agent count and scope before using subagents. Reuse that approval within its bounds; ask again before expanding the approved count or scope.

Investigate

  1. Capture the failing command or flow, input, expected behavior, actual result, and relevant environment. Read the error and nearby source before expanding the search.
  2. Reproduce the smallest useful case when practical. If reproduction is unavailable, distinguish what logs or source prove from what remains a hypothesis.
  3. Trace the symptom through the affected boundaries. Choose checks that distinguish plausible causes; independent observations can run together when safe. Avoid stacking speculative fixes.
  4. For a UI failure, inspect the supplied image or rendered state. Annotate only if it makes a material ambiguity easier to resolve; a screenshot does not automatically require a question.

Read debugging-playbook.md for deeper isolation, flaky failures, or temporary instrumentation. Keep sensitive data out of logs and remove temporary debug code.

Fix and verify

Make the smallest coherent fix, preserving unusual business rules unless evidence shows they are the defect. Avoid unrelated refactors. Add a regression test when it provides durable protection, ideally demonstrating failure before the fix.

Rerun the original failure and affected checks. Complete repository-required checks; broaden only when a remaining risk warrants it and authorization permits. Reuse valid results instead of asking routinely whether to run a full suite.

If measurement or test design becomes the main work, consult the relevant specialist directly when available. No coordinator handoff is needed merely to use another reference.

Report

Explain the cause, fix, and evidence that the failure is resolved. Include reproduction limits or remaining uncertainty. Record a difficult bug in existing durable docs only when future work would benefit.

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 Debugging Workflow AI skill do?

Reproduce, isolate, and fix unexplained failures, regressions, or flaky behavior. Use when the cause is uncertain; a known-cause fix usually needs only the normal implementation workflow.

Why use Debugging Workflow on TypingMind?

Because you install it once and use it with any model. Debugging Workflow 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 Workflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/thienanblog/awesome-ai-agent-skills/tree/main/plugins/project-development-skills/skills/debugging-workflow. 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 Debugging Workflow?

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 Workflow?

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

Is the Debugging Workflow AI skill free?

Yes. It is published on GitHub by thienanblog 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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