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Systematic Debugging

Community
jh941213
systematic-debugging

버그·테스트 실패·예상 밖 동작을 만나면 수정안을 제시하기 전에 사용. 근본 원인 규명 없이는 수정 금지

Overview

Publisherjh941213
Repositorymy-cc-harness
Skill namesystematic-debugging
Stars
125
Forks
35
Bundled files
Instructions only
Links
Skill sourceRepository
  • 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 jh941213 on GitHub. Read the source before you install it.

Installation

Install the Systematic Debugging 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/jh941213/my-cc-harness.git /tmp/my-cc-harness
mkdir -p .claude/skills
cp -r /tmp/my-cc-harness/skills/systematic-debugging .claude/skills/systematic-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Systematic Debugging 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 Systematic Debugging 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 Systematic Debugging 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 (체계적 디버깅)

철칙: 근본 원인 조사 없이 수정 금지. 증상만 고치는 것은 실패다.

간단해 보이는 버그, 급한 상황, "빨리 고쳐달라"는 압박일수록 이 프로세스를 지킨다 — 체계적 디버깅이 추측-수정 반복보다 빠르다.

4단계 (각 단계 완료 전 다음 단계 진행 금지)

1단계: 근본 원인 조사

  • 에러 메시지를 끝까지 읽는다 — 스택 트레이스 전체, 라인 번호, 에러 코드. 답이 이미 적혀 있는 경우가 많다
  • 일관되게 재현한다 — 재현 불가면 추측하지 말고 데이터를 더 모은다
  • 최근 변경 확인 — git diff, 최근 커밋, 의존성/설정/환경 차이
  • 다중 컴포넌트 시스템이면 계측 먼저 — 각 컴포넌트 경계에서 들어가는/나오는 데이터를 로그로 남겨 어느 레이어에서 깨지는지 증거를 확보한 뒤 그 레이어를 조사
  • 데이터 흐름 역추적 — 잘못된 값이 어디서 생겼는지 호출 스택을 거슬러 올라가 원천에서 수정 (증상 위치에서 수정 금지)

2단계: 패턴 분석

  • 같은 코드베이스에서 동작하는 유사 코드를 찾아 비교
  • 레퍼런스 구현이 있으면 전체를 읽는다 (스키밍 금지)
  • 동작하는 것과 깨진 것의 차이를 전부 나열 — "그건 상관없겠지" 가정 금지

3단계: 가설과 검증

  • 가설을 하나만, 명확하게: "X가 원인이라고 본다. 근거는 Y"
  • 최소 변경으로 검증 — 한 번에 한 변수만
  • 실패하면 새 가설 수립. 기존 수정 위에 수정을 쌓지 않는다
  • 모르면 "X를 이해하지 못했다"고 말한다. 아는 척 금지

4단계: 구현

  • 수정 전에 실패하는 테스트 케이스부터 만든다 (최소 재현)
  • 근본 원인에 대한 단일 수정 — "하는 김에" 리팩토링 금지
  • 수정 후 검증: 테스트 통과? 다른 테스트 안 깨짐?

3회 실패 규칙

수정을 3번 이상 시도했는데 실패하면 아키텍처를 의심한다. 수정할 때마다 다른 곳에서 새 문제가 나오면 가설이 아니라 구조가 틀린 것이다. 4번째 수정을 시도하지 말고 사용자와 구조 논의부터 한다.

위험 신호 (이 생각이 들면 STOP → 1단계로)

  • "일단 빨리 고치고 나중에 조사하자"
  • "X를 바꿔보고 되는지 보자"
  • "여러 개 한꺼번에 고치고 테스트 돌리자"
  • "테스트는 생략하고 수동으로 확인하자"
  • "완전히 이해는 안 되지만 이러면 될 것 같다"
  • 데이터 흐름 추적 전에 해결책부터 나열하고 있다

Frequently asked questions

What does the Systematic Debugging AI skill do?

버그·테스트 실패·예상 밖 동작을 만나면 수정안을 제시하기 전에 사용. 근본 원인 규명 없이는 수정 금지

Why use Systematic Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jh941213/my-cc-harness/tree/main/skills/systematic-debugging. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Systematic Debugging?

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 Systematic Debugging?

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

Is the Systematic Debugging AI skill free?

It is published on GitHub by jh941213. Check the repository for licensing terms. 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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