Debugger logo

Debugger

Organization
claude-world
debugger

Systematic debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.

Overview

Publisherclaude-world
Repositorydirector-mode-lite
Skill namedebugger
Stars
80
Forks
10
Bundled files
Instructions only
LicenseMIT
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by claude-world on GitHub. Read the source before you install it.

Installation

Install the Debugger 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/claude-world/director-mode-lite/tree/main/skills/debugger
  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/claude-world/director-mode-lite.git /tmp/director-mode-lite
mkdir -p .claude/skills
cp -r /tmp/director-mode-lite/skills/debugger .claude/skills/debugger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debugger 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 Debugger 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 Debugger 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.

Debugger Skill

Director Mode Lite - Debugging Specialist


5-Step Root-Cause Method

1. CAPTURE      → Collect the full error, reproduce, define expected vs actual
2. ISOLATE      → Narrow to the smallest failing case
3. HYPOTHESIZE  → List and rank likely causes
4. INVESTIGATE  → Test hypotheses, inspect state
5. FIX & VERIFY → Apply the minimal fix, confirm, prevent recurrence

Step 1: Capture

  • Collect the complete error message and stack trace
  • Can reproduce the issue with clear steps
  • Identify the input that triggered the error
  • Know expected vs actual behavior

Step 2: Isolate

  • Identify the failure location from the stack trace
  • Which file(s) and function(s) are involved?
  • When did it start? (git bisect, git log)
  • Narrow down to the smallest reproducible case

Step 3: Hypothesize

  • List possible causes based on evidence
  • Rank hypotheses by likelihood
  • Design a test to confirm or eliminate each

Step 4: Investigate

  • Analyze error messages and logs carefully
  • Add strategic debug logging; inspect variable state at key points
  • Check for: null/undefined values, type mismatches, race conditions, resource exhaustion, external service failures

Common Bug Patterns

PatternSignsCommon Fix
Null/UndefinedCannot read property of undefinedAdd null checks / optional chaining
Off-by-oneLoop runs one too many/few timesCheck loop bounds
Race conditionIntermittent failuresAdd synchronization
Type coercion"1" + 1 = "11"Explicit type conversion
Async issuesPromise { <pending> }Await/handle promises

By language and domain:

  • JavaScript/TypeScript: undefined is not a function (binding), Cannot read property of null (optional chaining), promise rejections (async/await handling), type errors (strict mode)
  • Python: AttributeError (init/typos), TypeError (type validation), ImportError (paths, circular imports), KeyError (dict defaults)
  • Database: connection timeouts (pool exhaustion), constraint violations (validation, foreign keys), deadlocks (transaction ordering)
  • API/Network: 4xx (request validation, auth), 5xx (server-side, resource limits), timeouts (network, long-running queries)

Investigation Tools

bash
# Search for error message
grep -r "error message" src/

# Find recent changes
git log --oneline -20
git diff HEAD~5

# Check specific function
grep -r "functionName" src/

Step 5: Fix & Verify

Apply the solution:

  • Make the minimal change; don't refactor while fixing; one fix per commit

Confirm the fix:

  • Original issue is resolved
  • No new issues introduced
  • Tests pass; add a test to prevent recurrence
  • Manual verification done

Output Format

markdown
## Debug Report

### Issue
[Description of the bug]

### Reproduction Steps
1. Step one
2. Step two
3. Observe error

### Root Cause
[Explanation of why this happens]

### Location
- **File**: `src/utils/parser.ts`
- **Line**: 45-52
- **Function**: `parseInput()`

### Fix Applied
[Description of the fix]

### Verification
- [x] Issue resolved
- [x] Tests pass
- [x] No regression

Frequently asked questions

What does the Debugger AI skill do?

Systematic debugging method: 5-step root-cause analysis (capture, isolate, hypothesize, investigate, fix & verify) plus common bug-pattern reference. Use when errors, exceptions, test failures, or unexpected behavior appear. Loaded automatically by the debugger agent.

Why use Debugger on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/claude-world/director-mode-lite/tree/main/skills/debugger. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debugger?

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 Debugger?

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

Is the Debugger AI skill free?

Yes. It is published on GitHub by claude-world 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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