name	project-init
description	Use when a non-developer user wants to create a new automation tool project. Sets up Python environment, project structure, and CLAUDE.md context file. Guides through environment setup → context gathering → structure initialization in user's preferred language.

Project Init

Overview

This skill initializes new automation tool projects for non-developers. It guides users through Python installation, gathers project context, and sets up a standardized documentation-driven project structure.

Use this skill when:

User wants to start a new automation project
User mentions creating a tool for their work
User asks how to begin with AI-assisted coding

Workflow

Step 1: Check Python Installation

Goal: Ensure Python 3.12+ is installed before proceeding.

Process:

Ask user about their operating system (if not already known):
- "어떤 운영체제를 사용하시나요? (Windows / macOS / Linux)"
- "Which operating system are you using? (Windows / macOS / Linux)"

Check if Python is installed:

python --version
# or
python3 --version

If Python is NOT installed or version < 3.12:
- Read the appropriate setup guide based on OS:
  - macOS: assets/SETUP-python-macos.md
  - Windows: assets/SETUP-python-windows.md
  - Linux: assets/SETUP-python-linux.md
- Present the setup instructions in user's preferred language
- Guide user through installation step by step
- Wait for user to complete installation and verify
If Python 3.12+ is installed:
- Confirm version: "Python 3.12.x가 설치되어 있습니다. 다음 단계로 진행하겠습니다."
- Proceed to Step 2

Important:

Language constraint explanation: Explain to user in their preferred language why we use Python:
- Best for data processing and automation
- Easy to install and use
- Rich ecosystem for common tasks
- TypeScript support can be added later if needed (e.g., n8n workflows)

Step 2: Gather User Context

Goal: Understand user's background, work domain, and project goals to personalize guidance.

Process:

Load the question reference:

Read: references/user-context-questions.md

Ask questions naturally (don't interrogate, have a conversation):
- Preferred communication language
- Work domain and tool purpose
- Technical background
- Confirm project type (Python-based local execution)

Example conversation flow:

AI: 안녕하세요! 어떤 분야에서 일하시나요?
    그리고 이 도구로 무엇을 자동화하고 싶으신가요?

User: 마케팅팀에서 일하고, 매일 리포트 만드는 걸 자동화하고 싶어요.

AI: 좋습니다! 프로그래밍 경험이 있으신가요?

User: 엑셀 함수는 쓸 줄 알지만 코딩은 처음이에요.

AI: 알겠습니다. Python 기반으로 컴퓨터에서 직접 실행되는 도구를
    만들게 됩니다. 먼저 프로젝트 구조를 만들겠습니다...

Store answers mentally (you'll use them in Step 3)

Reference: references/user-context-questions.md contains detailed question flow and examples.

Step 3: Create CLAUDE.md

Goal: Create a project-specific context file that will guide all future AI interactions.

Process:

Load the template:
```
Read: assets/CLAUDE.md.template
```
Fill in placeholders with answers from Step 2:
- {{PREFERRED_LANGUAGE}} → e.g., "한국어" or "English"
- {{WORK_DOMAIN}} → e.g., "마케팅"
- {{TOOL_PURPOSE}} → e.g., "매일 하는 리포트 자동화"
- {{TECHNICAL_BACKGROUND}} → e.g., "엑셀 함수는 쓸 줄 알지만 코딩은 처음"
- {{PROJECT_TYPE}} → "Python automation tool"
- {{DETAILED_TOOL_PURPOSE}} → More detailed explanation if user provided
- {{KEY_REQUIREMENTS}} → Specific requirements mentioned by user
- {{CONSTRAINTS}} → Any constraints (time, resources, etc.)

Write CLAUDE.md to project root:

Write: CLAUDE.md (with filled template)

Confirm with user:
- "CLAUDE.md 파일을 생성했습니다. 이 파일은 앞으로 AI가 프로젝트를 이해하는 데 사용됩니다."
- "Created CLAUDE.md file. This will help AI understand your project context."

Step 4: Initialize Project Structure

Goal: Create standardized directory structure for documentation-driven development.

Process:

Run the initialization script:

python scripts/init_structure.py

Or if user wants a specific directory:

python scripts/init_structure.py /path/to/project

The script creates:

├── docs/
│   ├── knowledge-base/  # Domain knowledge, setup guides, API docs
│   ├── plans/           # Implementation plans (PLN-XXX-YYYY-MM-DD-name.md)
│   └── architecture/    # Component descriptions (how things work)
├── src/                 # Source code
├── tests/               # Test files
└── .gitignore

Create .gitignore:

The init_structure.py script automatically copies the gitignore template.

Template includes:
- Python artifacts (pycache, *.pyc, etc.)
- Virtual environments (venv/, env/, etc.)
- IDE files (.vscode/, .idea/, etc.)
- Environment variables (.env, .env.local)
- Configuration files (config.yaml, config.json, credentials.json) ← Security!
- Test artifacts (.pytest_cache/, .coverage, etc.)

Initialize git repository (if not already initialized):

git init
git add .
git commit -m "Initial project structure"

Explain the structure to user in their preferred language:
- docs/ - 모든 지식은 여기에 문서로 저장됩니다
- docs/plans/ - 구현 계획이 PLN-001, PLN-002 형식으로 저장됩니다
- src/ - 실제 코드가 들어갑니다
- tests/ - 테스트 파일이 들어갑니다

Step 5: Next Steps Guidance

Goal: Guide user to the next skill (plan) and set expectations.

Process:

Explain the overall workflow:

프로젝트 초기화 완료! ✅

다음 단계:
1. [plan] - 무엇을 만들지 구체적으로 계획하기
2. [implement] - 계획을 코드로 구현하기
3. [debug] - 문제가 생기면 해결하기

"plan 스킬을 사용하고 싶습니다" 라고 말씀해주세요.

Remind about CLAUDE.md:
- "CLAUDE.md 파일은 프로젝트 루트에 있습니다"
- "앞으로 모든 AI 대화에서 이 파일을 참고합니다"
- "필요하면 언제든 수정할 수 있습니다"
Set expectations about Documentation = Source of Truth:
- 모든 지식은 문서(docs/)에 저장됩니다
- 코드는 블랙박스 구현입니다
- 변경사항은 문서에 먼저 또는 동시에 반영됩니다

Key Principles

Documentation-Driven Development

All knowledge lives in docs, code is black box implementation.

docs/knowledge-base/ - Domain knowledge, setup guides, API references
docs/plans/ - Implementation plans with Plan IDs (PLN-001, PLN-002, ...)
docs/architecture/ - How components work

Why?

Non-developers understand documentation better than code
AI can read docs to understand context without parsing code
Changes are tracked in human-readable format

Plan ID System

Format: PLN-001-YYYY-MM-DD-name.md
Auto-incremented (PLN-001, PLN-002, ...)
Used in git commit messages for traceability
Example: PLN-001-2025-10-26-daily-report-automation.md

Language Constraint (Python)

Month 1: Python only (TypeScript deferred based on demand)

Reasons explained to user:

Best for data processing and automation (primary use case)
Easy to install and use
Rich ecosystem for common automation tasks
TypeScript support can be added as an extension if needed (e.g., n8n workflows)

Communication Language

All communication with user must be in their preferred language (stored in CLAUDE.md).

If user starts in Korean → Korean
If user starts in English → English
If unclear → Ask: "어떤 언어로 대화하는 것이 편하세요? (한국어 / English)"

Troubleshooting

Python installation fails

macOS:

Homebrew permission issues → Don't use sudo, run brew doctor
Command not found → Restart terminal, check PATH

Windows:

"python not recognized" → Check "Add Python to PATH" was selected
Permission denied → Run as Administrator or change execution policy

Linux:

python3.12 not found → Add deadsnakes PPA (Ubuntu/Debian)
venv module missing → Install python3.12-venv package

Solution: Refer user to the appropriate SETUP guide and walk through step-by-step.

User has existing project structure

Don't overwrite!

Ask user if they want to:
- Create in new directory
- Merge with existing structure (careful!)
- Skip structure creation (only create CLAUDE.md)
If merging:
- Check for conflicts
- Ask permission before creating each directory
- Don't overwrite existing files

User wants TypeScript instead of Python

Explain the constraint:

Current version supports Python only
TypeScript can be added later as an extension
For n8n workflows, TypeScript can be integrated when needed

If user insists:

Acknowledge the limitation
Offer to proceed with Python and note TypeScript as future extension
Or defer to program facilitator for special cases

Resources Used

assets/

CLAUDE.md.template - Project context template
SETUP-python-macos.md - macOS Python 3.12 installation guide
SETUP-python-windows.md - Windows Python 3.12 installation guide
SETUP-python-linux.md - Linux Python 3.12 installation guide
gitignore-python.template - Python project .gitignore template

scripts/

init_structure.py - Cross-platform project structure initialization (Python)

references/

user-context-questions.md - Detailed user context gathering guide with examples

Example Session

User: 새 프로젝트 시작하고 싶어요

AI: [Loads project-init skill]

AI: 좋습니다! 먼저 Python이 설치되어 있는지 확인하겠습니다.
    어떤 운영체제를 사용하시나요? (Windows / macOS / Linux)

User: macOS요

AI: [Checks Python version]
    Python 3.12.2가 설치되어 있습니다. ✅

    이제 프로젝트에 대해 몇 가지 여쭤볼게요.
    어떤 분야에서 일하시나요? 그리고 이 도구로 무엇을 자동화하고 싶으신가요?

User: 마케팅팀에서 일하고, 매일 여러 플랫폼에서 데이터 모아서
      리포트 만드는데 이걸 자동화하고 싶어요

AI: 좋은 프로젝트네요! 프로그래밍 경험이 있으신가요?

User: 엑셀 함수는 쓸 줄 아는데 코딩은 처음이에요

AI: 알겠습니다. Python 기반으로 진행됩니다.
    - 데이터 처리와 자동화에 최적화되어 있어요
    - 설치와 사용이 쉽습니다
    - 나중에 필요하면 TypeScript도 추가할 수 있습니다

    [Creates CLAUDE.md with context]
    [Runs init_structure.py]
    [Creates .gitignore]
    [Initializes git]

    프로젝트 초기화 완료! ✅

    다음 단계:
    1. [plan] - 무엇을 만들지 구체적으로 계획하기
    2. [implement] - 계획을 코드로 구현하기
    3. [debug] - 문제가 생기면 해결하기

    "plan 스킬을 사용하고 싶습니다" 라고 말씀해주세요.

project-init

Install Skill

SKILL.md

Project Init

Overview

Workflow

Step 1: Check Python Installation

Step 2: Gather User Context

Step 3: Create CLAUDE.md

Step 4: Initialize Project Structure

Step 5: Next Steps Guidance

Key Principles

Documentation-Driven Development

Plan ID System

Language Constraint (Python)

Communication Language

Troubleshooting

Python installation fails

User has existing project structure

User wants TypeScript instead of Python

Resources Used

assets/

scripts/

references/

Example Session