Debug

OrganizationPopular

anthropics

debug

Structured debugging session — reproduce, isolate, diagnose, and fix. Trigger with an error message or stack trace, "this works in staging but not prod", "something broke after the deploy", or when behavior diverges from expected and the cause isn't obvious.

Overview

Publisher	anthropics
Repository	`knowledge-work-plugins`
Skill name	`debug`
Stars	24.9K
Forks	3K
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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 anthropics on GitHub. Read the source before you install it.

Installation

Install the Debug 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.
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.

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/anthropics/knowledge-work-plugins.git /tmp/knowledge-work-plugins
mkdir -p .claude/skills
cp -r /tmp/knowledge-work-plugins/engineering/skills/debug .claude/skills/debug

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

Use it in TypingMind

Enable 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 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 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.

/debug

If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.

Run a structured debugging session to find and fix issues systematically.

Usage


/debug $ARGUMENTS

How It Works


┌─────────────────────────────────────────────────────────────────┐
│                       DEBUG                                        │
├─────────────────────────────────────────────────────────────────┤
│  Step 1: REPRODUCE                                                │
│  ✓ Understand the expected vs. actual behavior                   │
│  ✓ Identify exact reproduction steps                             │
│  ✓ Determine scope (when did it start? who is affected?)        │
│                                                                    │
│  Step 2: ISOLATE                                                   │
│  ✓ Narrow down the component, service, or code path             │
│  ✓ Check recent changes (deploys, config changes, dependencies) │
│  ✓ Review logs and error messages                                │
│                                                                    │
│  Step 3: DIAGNOSE                                                  │
│  ✓ Form hypotheses and test them                                 │
│  ✓ Trace the code path                                           │
│  ✓ Identify root cause (not just symptoms)                      │
│                                                                    │
│  Step 4: FIX                                                       │
│  ✓ Propose a fix with explanation                                │
│  ✓ Consider side effects and edge cases                          │
│  ✓ Suggest tests to prevent regression                           │
└─────────────────────────────────────────────────────────────────┘

What I Need From You

Tell me about the problem. Any of these help:

Error message or stack trace
Steps to reproduce
What changed recently
Logs or screenshots
Expected vs. actual behavior

Output

markdown
## Debug Report: [Issue Summary]

### Reproduction
- **Expected**: [What should happen]
- **Actual**: [What happens instead]
- **Steps**: [How to reproduce]

### Root Cause
[Explanation of why the bug occurs]

### Fix
[Code changes or configuration fixes needed]

### Prevention
- [Test to add]
- [Guard to put in place]

If Connectors Available

If ~~monitoring is connected:

Pull logs, error rates, and metrics around the time of the issue
Show recent deploys and config changes that may correlate

If ~~source control is connected:

Identify recent commits and PRs that touched affected code paths
Check if the issue correlates with a specific change

If ~~project tracker is connected:

Search for related bug reports or known issues
Create a ticket for the fix once identified

Tips

Share error messages exactly — Don't paraphrase. The exact text matters.
Mention what changed — Recent deploys, dependency updates, and config changes are top suspects.
Include context — "This works in staging but not prod" or "Only affects large payloads" narrows things fast.

Frequently asked questions

What does the Debug AI skill do?

Why use Debug on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/anthropics/knowledge-work-plugins/tree/main/engineering/skills/debug. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use 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 Debug?

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

Is the Debug AI skill free?

Yes. It is published on GitHub by anthropics 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

/debug

Usage

How It Works

What I Need From You

Output

If Connectors Available

Tips

Frequently asked questions

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