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Trace And Isolate

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rohitg00
trace-and-isolate

Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and bash.

Overview

Publisherrohitg00
Repositoryskillkit
Skill nametrace-and-isolate
Stars
1.5K
Forks
147
Bundled files
Instructions only
LicenseApache-2.0
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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Trace And Isolate 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/rohitg00/skillkit.git /tmp/skillkit
mkdir -p .claude/skills
cp -r /tmp/skillkit/packages/core/src/methodology/packs/debugging/trace-and-isolate .claude/skills/trace-and-isolate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Trace And Isolate 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 Trace And Isolate 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 Trace And Isolate 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.

Trace and Isolate

You are using systematic tracing and isolation techniques to narrow down where a bug originates. The goal is to find the exact location in code where behavior diverges from expectation.

Quick Reference

ScenarioTechnique
Large codebase / complex flowBinary search debugging
Bug appeared between commitsgit bisect
Unclear data transformationStrategic [TRACE] logging
Which component is faulty?Component isolation + mocks
Bug hard to reproduceMinimal reproduction case
Conditional or intermittent bugConditional breakpoints / watch expressions

Binary Search Debugging

When you have a large codebase or complex flow:

  1. Identify the start - Last known good state
  2. Identify the end - First observed bad state
  3. Test the middle - Check if behavior is good or bad
  4. Repeat - Binary search on the half with the bug

Code Path Binary Search

Start: User clicks submit button
End: Error shown to user

Midpoint 1: Form validation
→ Data looks correct here? Continue to later code
→ Data already wrong? Focus on earlier code

Midpoint 2: API request construction
→ Request payload correct? Focus on server side
→ Payload already malformed? Focus on form handling

...Continue until exact line is found

Git Bisect for Regressions

When a bug appeared between two commits:

bash
# Start bisect
git bisect start

# Mark current (broken) state as bad
git bisect bad

# Mark last known good state
git bisect good v2.3.0

# Git checks out middle commit, test and mark
git bisect good  # or git bisect bad

# Repeat until found
# Git will report: "abc123 is the first bad commit"

# Clean up
git bisect reset

Automated bisect with test script:

bash
git bisect start HEAD v2.3.0
git bisect run npm test -- --grep "failing test"

Tracing Techniques

Strategic Logging

Add temporary logging at key points:

typescript
function processOrder(order) {
  console.log('[TRACE] processOrder input:', JSON.stringify(order));

  const validated = validateOrder(order);
  console.log('[TRACE] after validation:', JSON.stringify(validated));

  const priced = calculatePrice(validated);
  console.log('[TRACE] after pricing:', JSON.stringify(priced));

  const result = submitOrder(priced);
  console.log('[TRACE] final result:', JSON.stringify(result));

  return result;
}

Log template: [TRACE] <location>: <what> = <value>

Data Flow Tracing

Track how data transforms through the system:

Input: { userId: "123", items: [...] }
validateUser() → { userId: "123", verified: true }
enrichItems() → { userId: "123", verified: true, items: [...enriched] }
calculateTotals() → { ..., subtotal: 100, tax: 8, total: 108 }
Output: { orderId: "456", total: 108 }

At each step, verify:

  • Is the input what you expected?
  • Is the output what you expected?
  • Where does expected diverge from actual?

Control Flow Tracing

Track which code paths execute:

typescript
function handleRequest(req) {
  console.log('[TRACE] handleRequest entered');

  if (req.authenticated) {
    console.log('[TRACE] authenticated path');
    if (req.isAdmin) {
      console.log('[TRACE] admin path');
      return handleAdminRequest(req);
    } else {
      console.log('[TRACE] user path');
      return handleUserRequest(req);
    }
  } else {
    console.log('[TRACE] unauthenticated path');
    return handlePublicRequest(req);
  }
}

Isolation Techniques

Component Isolation

Test components in isolation to determine which is faulty:

Full System: Frontend → API → Database
Test 1: Frontend → Mock API
        → Works? Problem is in API or Database

Test 2: Real API → Mock Database
        → Works? Problem is in Database

Test 3: API with minimal data
        → Works? Problem is data-dependent

Minimal Reproduction

Strip away everything non-essential:

  1. Remove unrelated code - Comment out or delete
  2. Simplify data - Use minimal test data
  3. Remove dependencies - Mock external services
  4. Reduce scope - Single function/component

Goal: Smallest possible code that still shows the bug

Environment Isolation

Eliminate environmental factors:

  • Same behavior in different browsers?
  • Same behavior on different machines?
  • Same behavior with fresh data?
  • Same behavior after clearing cache?
  • Same behavior with default config?

Breakpoint Strategies

Strategic Breakpoint Placement

typescript
function complexFunction(input) {
  // BREAKPOINT 1: Entry - check input
  const step1 = transform(input);
  // BREAKPOINT 2: After first transformation

  for (const item of step1.items) {
    // BREAKPOINT 3: Inside loop - conditional on item
    process(item);
  }

  // BREAKPOINT 4: Exit - check output
  return finalize(step1);
}

Conditional Breakpoints

Only break when condition is met:

  • item.id === "problematic-id"
  • count > 100
  • response.status !== 200

Watch Expressions

Monitor values without stopping:

  • this.state.items.length
  • performance.now() - startTime
  • Object.keys(cache).length

Isolation Checklist

Before declaring a component faulty:

  • Tested in complete isolation?
  • All inputs verified correct?
  • All dependencies mocked/verified?
  • Tested with known-good data?
  • Reproduced on clean environment?

Common Isolation Patterns

Database Isolation

sql
-- Create isolated test data
BEGIN TRANSACTION;
-- Insert test data
-- Run test queries
-- Verify results
ROLLBACK;

Network Isolation

typescript
// Intercept and log all network requests
const originalFetch = window.fetch;
window.fetch = async (...args) => {
  console.log('[TRACE] fetch:', args[0]);
  const response = await originalFetch(...args);
  console.log('[TRACE] response:', response.status);
  return response;
};

Time Isolation

typescript
// Control time for debugging
const realNow = Date.now;
Date.now = () => {
  const time = realNow();
  console.log('[TRACE] Date.now():', new Date(time).toISOString());
  return time;
};

When to Move On

Stop isolating when you have:

  • Exact file and line number
  • Minimal reproduction case
  • Clear understanding of trigger conditions
  • Evidence for root cause hypothesis

Frequently asked questions

What does the Trace And Isolate AI skill do?

Applies systematic tracing and isolation techniques to pinpoint exactly where a bug originates in code. Use when a bug is hard to locate, code is not working as expected, an error or crash appears with unclear cause, a regression was introduced between recent commits, or you need to narrow down which component, function, or line is faulty. Covers binary search debugging, git bisect for regressions, strategic logging with [TRACE] patterns, data and control flow tracing, component isolation, minimal reproduction cases, conditional breakpoints, and watch expressions across TypeScript, SQL, and...

Why use Trace And Isolate on TypingMind?

Because you install it once and use it with any model. Trace And Isolate 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 Trace And Isolate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/skillkit/tree/main/packages/core/src/methodology/packs/debugging/trace-and-isolate. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Trace And Isolate?

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 Trace And Isolate?

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

Is the Trace And Isolate AI skill free?

Yes. It is published on GitHub by rohitg00 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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