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Claude Debug

Community
xiaolai
claude-debug

Send a bug or failing test to Claude Code for root-cause analysis and fix. Use when you've hit an error you can't trace, a test is failing for unclear reasons, or a behavior is wrong and the cause is deep in the codebase.

Overview

Publisherxiaolai
Repositorycc-suite
Skill nameclaude-debug
Stars
56
Forks
21
Bundled files
1
LicenseISC
Links
  • 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 xiaolai on GitHub. Read the source before you install it.

Installation

Install the Claude 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.
    https://github.com/xiaolai/cc-suite/tree/main/skills/cc-suite/claude-debug
  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/xiaolai/cc-suite.git /tmp/cc-suite
mkdir -p .claude/skills
cp -r /tmp/cc-suite/skills/cc-suite/claude-debug .claude/skills/claude-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Claude 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 Claude 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 Claude 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.

Claude Debug

Delegate a debugging session to Claude Code — Claude traces the root cause and applies a fix.

When to Use

  • When a test is failing and the cause is unclear
  • When asked to "have Claude debug this"
  • When an error trace points deep into framework code or a complex call chain
  • When you've already tried the obvious fixes and need fresh eyes

Call Pattern

Step 1: Send the bug report

mcp__claude-code__claude_code:
  prompt: |
    Debug the following issue and fix it.

    SYMPTOM: {error message, failing test name, or wrong behavior description}

    ERROR OUTPUT:
    ```
    {paste stack trace or test output here}
    ```

    REPRODUCTION STEPS: {how to reproduce — command to run, inputs, etc.}

    WHAT I TRIED: {optional — what you already ruled out}

    Instructions:
    1. Read the source files involved in the reported code flow
    2. Identify the root cause (not just where it crashes — why)
    3. Apply a minimal, correct fix
    4. Run the failing test/command to confirm the fix works
    5. Check for regressions in related tests
    6. Report: root cause, fix applied, verification result

    PROVENANCE NOTE: The code being debugged was written by OpenAI Codex. Evaluate
    with full rigor — do not assume Codex's code is correct anywhere adjacent to the
    reported bug. Check surrounding logic independently.
  cwd: {project working directory}
  effort: high

Save the returned session_id as {debug_session_id}.

Step 2: Follow up

If Claude identified the root cause but couldn't fix it cleanly, or found related issues:

mcp__claude-code__claude_code_reply:
  session_id: {debug_session_id}
  prompt: "You identified the root cause in parseConfig(). Also check validateConfig() — it has a similar pattern."

Output Format

Report to the user:

  • Root cause (one sentence)
  • Fix applied (file:line, what changed)
  • Verification result (test output)
  • Any adjacent issues Claude found

Failure Handling

A Claude-backed call that does not return a usable result is terminal for this skill. Stop and report; do not improvise a way to keep going.

This covers every non-success outcome: a runtime or transport error, a timeout, a rate limit, an empty or truncated result, and a result that does not match the shape this skill asked for. Absence of an error is not success — a call that returns nothing has failed, however it exited.

Never report a failed call as a result. It is not CLEAN, not "no findings", not a completed verification, not an empty plan. A verification that did not run is UNVERIFIED, and saying so is the entire value of the step.

Do not recover on your own initiative. Unless the user asks for it: do not retry the call, do not open a replacement session, do not switch to another Claude execution path (a different MCP tool, a different skill, a direct invocation), and do not fall back to doing the work yourself in place of the delegate. A failed claude_code_reply in particular must not silently become a new claude_code session — that discards the conversation the reply was meant to continue, and the original work may still be running.

Preserve the session. Report any session_id the call returned, even on failure, so the user can resume that exact session rather than start over.

Say what may still be running. A timeout or transport error does not establish that the work on the other side stopped. Say so explicitly, so the user does not start a second attempt that overlaps the first.

Then stop according to what the call could have changed:

Call classOn failure
Read-only (claude-plan, claude-review, audit, verify, advisor *_consult)Report the operation as blocked, with the error and any session_id. Nothing was changed.
Write-capable (claude-debug, claude-implement)Stop before any further delegation and state explicitly whether partial workspace changes may exist — the delegate may have written files before failing. Recommend the user inspect git status / git diff.
Cycle (audit-fix)Stop the current cycle. Retain the existing findings and their fix status; do not discard them and do not mark unattempted findings as resolved. Report which finding the cycle stopped on.

Notes

  • Always pass the exact error output — Claude needs the full stack trace, not a summary
  • effort: high is important for non-obvious bugs; shallow effort stops at symptoms
  • If Claude can't reproduce the bug, ask it to add diagnostic logging first, then re-run
  • permissionMode defaults to default (allows writes) — Claude will apply the fix directly

Related Skills

  • claude-plan — use before debugging to sketch a diagnosis strategy for complex, multi-file issues
  • claude-implement — use after root cause is identified to apply a larger fix or refactor
  • $audit-fix — use when the bug is a pattern across many files and needs a full audit→fix cycle

Example Invocations

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 Claude Debug AI skill do?

Send a bug or failing test to Claude Code for root-cause analysis and fix. Use when you've hit an error you can't trace, a test is failing for unclear reasons, or a behavior is wrong and the cause is deep in the codebase.

Why use Claude Debug on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/xiaolai/cc-suite/tree/main/skills/cc-suite/claude-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 Claude 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 Claude Debug?

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

Is the Claude Debug AI skill free?

Yes. It is published on GitHub by xiaolai under the ISC 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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