scaffold

Create a new project using the specified template: $ARGUMENTS

Output Format: Use clear section headers, timestamps for each step, and progress indicators for setup operations.

Tool Validation: First verify that required tools are installed (git, uv, gh) and the target directory is valid.

Python Project Defaults: All Python projects use UV for virtual environment management and package installation. Never use pip, pipenv, or conda.

Template Options:

• python-lib - Python library with pyproject.toml, tests, CI/CD
• python-cli - Python CLI application with Click/Typer
• python-web - FastAPI web application
• python-data - Data science project with Jupyter notebooks
• python-langchain - LangChain agent project with LangGraph support
• python-adk - Google Agent Development Kit multi-agent system
• python-crewai - CrewAI multi-agent orchestration framework
• python-autogen - Microsoft AutoGen conversational agents
• python-swarm - OpenAI Swarm lightweight agent framework
• python-phidata - Phidata AI assistant framework
• python-llama-index - LlamaIndex RAG agents with document indexing
• python-haystack - Deepset Haystack NLP pipelines
• python-semantic-kernel - Microsoft Semantic Kernel plugin architecture
• python-agency-swarm - Agency Swarm hierarchical agent framework
• node-lib - Node.js library with TypeScript
• node-web - React/Next.js web application
• rust-lib - Rust library with Cargo.toml
• go-cli - Go CLI application
• custom - Interactive template selection

Template Loading: Read the template definition from ${CLAUDE_SKILL_DIR}/templates/<template-name>.md for the selected template's directory structure and dependencies.

Project Setup Steps:

1. Template Selection: Determine template from argument or prompt interactively
2. Load Template: Read the template file from ${CLAUDE_SKILL_DIR}/templates/<template-name>.md
3. Directory Creation: Create project directory with proper naming conventions
4. Template Scaffolding: Create the directory structure and files defined in the template
5. Dependency Management:
   • For Python: Use uv init and uv add exclusively for all dependencies
   • For Python: Create virtual environment with uv venv and activate with uv run
   • For other languages: Install language-specific dependencies
6. Git Initialization: Initialize repo, create .gitignore, initial commit
7. CI/CD Setup: Create GitHub Actions workflows, testing and linting pipelines
8. Development Environment: Setup IDE config files, development scripts
9. Documentation: Generate README, CONTRIBUTING.md, documentation framework
10. Testing Framework: Setup test directory, sample tests, configure runners
11. Quality Tools: Configure linting, formatters, type checking
12. Final Validation:
    • For Python: Run uv run python -m pytest and uv build
    • For other languages: Run initial build/compile and basic tests

Interactive Prompts:

• Project name (default: directory name)
• Author name and email
• License type (MIT, Apache, etc.)
• Description
• Python version (for Python projects)

Template Variables:

• {{PROJECT_NAME}} - Project name
• {{AUTHOR_NAME}} - Author name
• {{AUTHOR_EMAIL}} - Author email
• {{DESCRIPTION}} - Project description
• {{LICENSE}} - License type
• {{YEAR}} - Current year
• {{PYTHON_VERSION}} - Python version

Error Handling: If any step fails, attempt to fix the issue automatically. If unable to fix, report the specific error and provide cleanup instructions.

Post-Setup Instructions:

• Display next steps for development
• Show available commands (test, build, etc.)
• Provide quick start guide
• Suggest development workflow