Root Cause Tracing logo

Root Cause Tracing

OrganizationPopular
NeoLabHQ
root-cause-tracing

Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

Overview

PublisherNeoLabHQ
Repositorycontext-engineering-kit
Skill nameroot-cause-tracing
Stars
1.7K
Forks
159
Bundled files
Instructions only
LicenseGPL-3.0
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 NeoLabHQ on GitHub. Read the source before you install it.

Installation

Install the Root Cause Tracing 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/NeoLabHQ/context-engineering-kit.git /tmp/context-engineering-kit
mkdir -p .claude/skills
cp -r /tmp/context-engineering-kit/antigravity/skills/root-cause-tracing .claude/skills/root-cause-tracing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Root Cause Tracing 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 Root Cause Tracing 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 Root Cause Tracing 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.

Root Cause Tracing

Overview

Bugs often manifest deep in the call stack (git init in wrong directory, file created in wrong location, database opened with wrong path). Your instinct is to fix where the error appears, but that's treating a symptom.

Core principle: Trace backward through the call chain until you find the original trigger, then fix at the source.

When to Use

dot
digraph when_to_use {
    "Bug appears deep in stack?" [shape=diamond];
    "Can trace backwards?" [shape=diamond];
    "Fix at symptom point" [shape=box];
    "Trace to original trigger" [shape=box];
    "BETTER: Also add defense-in-depth" [shape=box];

    "Bug appears deep in stack?" -> "Can trace backwards?" [label="yes"];
    "Can trace backwards?" -> "Trace to original trigger" [label="yes"];
    "Can trace backwards?" -> "Fix at symptom point" [label="no - dead end"];
    "Trace to original trigger" -> "BETTER: Also add defense-in-depth";
}

Use when:

  • Error happens deep in execution (not at entry point)
  • Stack trace shows long call chain
  • Unclear where invalid data originated
  • Need to find which test/code triggers the problem

The Tracing Process

1. Observe the Symptom

Error: git init failed in /Users/jesse/project/packages/core

2. Find Immediate Cause

What code directly causes this?

typescript
await execFileAsync('git', ['init'], { cwd: projectDir });

3. Ask: What Called This?

typescript
WorktreeManager.createSessionWorktree(projectDir, sessionId)
  → called by Session.initializeWorkspace()
  → called by Session.create()
  → called by test at Project.create()

4. Keep Tracing Up

What value was passed?

  • projectDir = '' (empty string!)
  • Empty string as cwd resolves to process.cwd()
  • That's the source code directory!

5. Find Original Trigger

Where did empty string come from?

typescript
const context = setupCoreTest(); // Returns { tempDir: '' }
Project.create('name', context.tempDir); // Accessed before beforeEach!

Adding Stack Traces

When you can't trace manually, add instrumentation:

typescript
// Before the problematic operation
async function gitInit(directory: string) {
  const stack = new Error().stack;
  console.error('DEBUG git init:', {
    directory,
    cwd: process.cwd(),
    nodeEnv: process.env.NODE_ENV,
    stack,
  });

  await execFileAsync('git', ['init'], { cwd: directory });
}

Critical: Use console.error() in tests (not logger - may not show)

Run and capture:

bash
npm test 2>&1 | grep 'DEBUG git init'

Analyze stack traces:

  • Look for test file names
  • Find the line number triggering the call
  • Identify the pattern (same test? same parameter?)

Finding Which Test Causes Pollution

If something appears during tests but you don't know which test:

Use the bisection script: @find-polluter.sh

bash
./find-polluter.sh '.git' 'src/**/*.test.ts'

Runs tests one-by-one, stops at first polluter. See script for usage.

Real Example: Empty projectDir

Symptom: .git created in packages/core/ (source code)

Trace chain:

  1. git init runs in process.cwd() ← empty cwd parameter
  2. WorktreeManager called with empty projectDir
  3. Session.create() passed empty string
  4. Test accessed context.tempDir before beforeEach
  5. setupCoreTest() returns { tempDir: '' } initially

Root cause: Top-level variable initialization accessing empty value

Fix: Made tempDir a getter that throws if accessed before beforeEach

Also added defense-in-depth:

  • Layer 1: Project.create() validates directory
  • Layer 2: WorkspaceManager validates not empty
  • Layer 3: NODE_ENV guard refuses git init outside tmpdir
  • Layer 4: Stack trace logging before git init

Key Principle

dot
digraph principle {
    "Found immediate cause" [shape=ellipse];
    "Can trace one level up?" [shape=diamond];
    "Trace backwards" [shape=box];
    "Is this the source?" [shape=diamond];
    "Fix at source" [shape=box];
    "Add validation at each layer" [shape=box];
    "Bug impossible" [shape=doublecircle];
    "NEVER fix just the symptom" [shape=octagon, style=filled, fillcolor=red, fontcolor=white];

    "Found immediate cause" -> "Can trace one level up?";
    "Can trace one level up?" -> "Trace backwards" [label="yes"];
    "Can trace one level up?" -> "NEVER fix just the symptom" [label="no"];
    "Trace backwards" -> "Is this the source?";
    "Is this the source?" -> "Trace backwards" [label="no - keeps going"];
    "Is this the source?" -> "Fix at source" [label="yes"];
    "Fix at source" -> "Add validation at each layer";
    "Add validation at each layer" -> "Bug impossible";
}

NEVER fix just where the error appears. Trace back to find the original trigger.

Stack Trace Tips

In tests: Use console.error() not logger - logger may be suppressed Before operation: Log before the dangerous operation, not after it fails Include context: Directory, cwd, environment variables, timestamps Capture stack: new Error().stack shows complete call chain

Real-World Impact

From debugging session (2025-10-03):

  • Found root cause through 5-level trace
  • Fixed at source (getter validation)
  • Added 4 layers of defense
  • 1847 tests passed, zero pollution

Frequently asked questions

What does the Root Cause Tracing AI skill do?

Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

Why use Root Cause Tracing on TypingMind?

Because you install it once and use it with any model. Root Cause Tracing 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 Root Cause Tracing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/NeoLabHQ/context-engineering-kit/tree/master/antigravity/skills/root-cause-tracing. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Root Cause Tracing?

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 Root Cause Tracing?

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

Is the Root Cause Tracing AI skill free?

Yes. It is published on GitHub by NeoLabHQ under the GPL-3.0 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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