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Debug Systematic

Community
travisjneuman
debug-systematic

Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.

Overview

Publishertravisjneuman
Repository.claude
Skill namedebug-systematic
Stars
98
Forks
22
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 travisjneuman on GitHub. Read the source before you install it.

Installation

Install the Debug Systematic 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/travisjneuman/.claude.git /tmp/.claude
mkdir -p .claude/skills
cp -r /tmp/.claude/skills/debug-systematic .claude/skills/debug-systematic
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debug Systematic 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 Systematic 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 Systematic 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.

Systematic Debugging Protocol

A disciplined, evidence-based approach to debugging that prevents guessing and ensures root cause discovery.

The 4-Phase Protocol

Phase 1: REPRODUCE (Establish Ground Truth)

Goal: Create reliable reproduction steps before ANY investigation.

Actions:

  1. Document exact steps to trigger the bug
  2. Record environment specifics (OS, versions, config, memory, network)
  3. Determine frequency: Always? Sometimes? Specific conditions?
  4. Capture exact error messages, stack traces, screenshots
  5. Test on different environments to isolate variables

Key Questions:

  • When did it last work correctly?
  • What changed since then? (code, deps, config, infrastructure)
  • Is it environment-specific?
  • Is it data-specific?
  • Is it timing-specific?

Output: Clear reproduction steps that reliably trigger the issue.


Phase 2: ISOLATE (Narrow the Scope)

Goal: Reduce the search space from "entire codebase" to "specific component."

Techniques:

Binary Search:

  1. Identify two points: working state and broken state
  2. Test the midpoint
  3. Recurse into the broken half
  4. Continue until the change is identified

Git Bisect (for regressions):

bash
git bisect start
git bisect bad HEAD
git bisect good <known-good-commit>
# Git will checkout commits for testing
# After each test:
git bisect good  # or git bisect bad
# Continue until culprit found

Code Elimination:

  • Comment out sections to isolate the problem
  • Create minimal reproduction case
  • Strip away everything non-essential

Environment Isolation:

  • Test in isolation (unit test the failing path)
  • Compare working vs broken environments
  • Use fresh installs to eliminate pollution

Output: "The bug is in [specific component/function/line range]"


Phase 3: DIAGNOSE (Understand Root Cause)

Goal: Know exactly WHY the bug occurs, not just WHERE.

Scientific Method:

  1. Observe: What exactly is happening?
  2. Hypothesize: Why might this be happening?
  3. Predict: If hypothesis is correct, what else would be true?
  4. Test: Verify predictions with evidence
  5. Iterate: Refine hypothesis based on results

Logging Strategy:

javascript
// Add strategic logging at boundaries
console.log("[DEBUG] Function entry:", { input, state });
console.log("[DEBUG] After processing:", { result, sideEffects });
console.log("[DEBUG] Function exit:", { returnValue });

Common Root Causes:

SymptomLikely Causes
Works locally, fails in CIEnvironment differences, timing, resources
Intermittent failureRace condition, flaky network, resource contention
Works then stops workingState mutation, memory leak, cache poisoning
Wrong dataType coercion, encoding, timezone, precision
Silent failureSwallowed exception, async error, missing await

Output: Clear explanation of the root cause with evidence.


Phase 4: FIX & VERIFY (Resolve and Prevent)

Goal: Fix the issue and prevent regression.

Fix Process:

  1. Write a failing test that captures the bug
  2. Implement minimal fix - change as little as possible
  3. Verify test passes - confirms fix works
  4. Check for similar patterns - same bug elsewhere?
  5. Review fix for side effects - does it break anything?
  6. Document the fix - why it happened, how to prevent

Verification Checklist:

  • Test passes that specifically catches this bug
  • Existing tests still pass
  • Manual verification confirms fix
  • Fix works in all affected environments
  • No new warnings or errors introduced

Prevention:

  • Add guards/validation at boundaries
  • Improve error messages for easier future debugging
  • Document gotchas for other developers
  • Consider if architectural change prevents similar bugs

Debugging Anti-Patterns

DO NOT:

  • Guess and hope (change things randomly)
  • Assume you know the problem without evidence
  • Trust comments/docs over actual code behavior
  • Debug production with print statements you'll forget to remove
  • Fix the symptom instead of the root cause
  • Make multiple changes at once

DO:

  • Verify assumptions with evidence
  • Change one thing at a time
  • Log actual values, not what you expect
  • Trust the code over documentation
  • Take breaks when stuck (fresh eyes help)

Quick Reference

1. REPRODUCE → Can I reliably trigger this?
2. ISOLATE   → Where exactly is it failing?
3. DIAGNOSE  → Why is it failing?
4. FIX       → How do I fix it permanently?

Output Template

markdown
## Bug Investigation: [Title]

### Reproduction

- Steps to reproduce
- Environment details
- Frequency

### Isolation

- Search method used
- Scope narrowed to

### Root Cause

- What's actually wrong
- Why it happens
- Evidence

### Fix

- Code changes made
- Test added

### Prevention

- How to prevent similar bugs
- Documentation updates

Frequently asked questions

What does the Debug Systematic AI skill do?

Systematic 4-phase debugging methodology for complex, intermittent, or mysterious issues. Use when investigating bugs, race conditions, or unexplained failures.

Why use Debug Systematic on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/travisjneuman/.claude/tree/master/skills/debug-systematic. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Debug Systematic?

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 Systematic?

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

Is the Debug Systematic AI skill free?

Yes. It is published on GitHub by travisjneuman 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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