skillforge-cli 0.1.1

AI-powered CLI tool for interactive, personalized learning experiences

AI-powered interactive learning CLI for developers

Features • Installation • Usage • Architecture • Contributing

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SkillForge helps developers learn new frameworks, tools, and technologies through hands-on practice in safe, simulated environments. Get personalized courses, step-by-step guidance, and real-time feedback—all from your terminal.

Status

🚧 Active Development — Interactive learning sessions now available!

Features

AI-Powered Course Generation — Uses Claude or GPT to create personalized curricula with intelligent caching

Safe Command Simulation — Practice shell, Python, git, Docker, and kubectl without risk to your system

Intelligent Validation — Pattern matching + LLM evaluation with progressive hints and feedback

Progress Persistence — Save and resume sessions at any time

Rich Terminal UI — Beautiful course overviews, progress indicators, and formatted output

Intelligent Feedback

SkillForge provides helpful feedback whether you get it right or need another try:

Installation

Install from PyPI

pip install skillforge-cli
# or
pipx install skillforge-cli

Then set up your API key and start learning:

export ANTHROPIC_API_KEY=sk-ant-...
skillforge learn "python basics"

Development Setup

Requirements: Python 3.12 or higher

1. Clone the repository

   git clone https://github.com/milank94/skillforge.git
   cd skillforge
   

2. Create a virtual environment

   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   

3. Install in editable mode

   pip install --upgrade pip
   pip install -e ".[dev]"
   

4. Set up API key

   export ANTHROPIC_API_KEY=sk-ant-...
   # or
   export OPENAI_API_KEY=sk-...
   

5. Verify installation

   skillforge --version
   skillforge --help
   

Usage

Generate a Course

# Start a learning session (uses Anthropic by default)
skillforge learn "pytorch basics"

# Specify difficulty and lesson count
skillforge learn "docker fundamentals" --difficulty intermediate --lessons 7

# Use OpenAI instead
skillforge learn "kubernetes" --provider openai

# Non-interactive mode (just display the course)
skillforge learn "git basics" --no-interactive

📸 Example: Generated PyTorch Course

Resume & Progress

# List saved sessions
skillforge resume

# Resume a specific session (supports partial IDs)
skillforge resume abc123

# View progress without resuming
skillforge status abc123

📸 Example: Resume Menu

Cache Management

# View cache info
skillforge cache-info

# Clear cached courses
skillforge cache-clear

Configuration

Environment Variables

ANTHROPIC_API_KEY=sk-ant-...       # Anthropic API key
OPENAI_API_KEY=sk-...              # OpenAI API key
SKILLFORGE_LLM_PROVIDER=anthropic  # Default provider (anthropic or openai)
SKILLFORGE_MODEL=claude-sonnet-4-5-20250929  # Model to use
SKILLFORGE_TEMPERATURE=0.7         # Generation temperature
SKILLFORGE_DATA_DIR=~/.skillforge  # Data directory

Development

Running Tests

# Run all tests
pytest

# Run with verbose output
pytest -v

# Run specific test file
pytest tests/test_simulator.py

# Run integration tests (requires API keys)
pytest -m integration

Code Quality

# Format code
black skillforge/ tests/

# Lint code
ruff check skillforge/ tests/

# Type checking
mypy skillforge/

Architecture

┌─────────────────────────────────────────────┐
│              CLI Interface                   │
│           (typer + rich)                     │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────┴──────────────────────────┐
│           Course Generator                   │
│     (LLM-powered, hash-based caching)       │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────┴──────────────────────────┐
│          Command Simulator                   │
│  (pattern matching + LLM fallback)          │
│  Virtual file system, shell, Python,        │
│  git, docker, kubectl simulation            │
└──────────────────┬──────────────────────────┘
                   │
┌──────────────────┴──────────────────────────┐
│          Exercise Validator                  │
│  (pattern matching + LLM evaluation)        │
│  Progressive hints, score feedback          │
└─────────────────────────────────────────────┘

Roadmap

• Phase 1: Basic package setup and CLI structure
• Phase 2: Data models (Course, Lesson, Exercise, Progress)
• Phase 3: LLM integration (course generator, simulator, validator)
• Phase 4: Interactive learning sessions and progress tracking

Project Structure

skillforge/
├── skillforge/              # Main package
│   ├── __init__.py          # Package initialization
│   ├── cli.py               # CLI interface (typer + rich)
│   ├── core/                # Core functionality
│   │   ├── course_generator.py  # LLM-based course creation
│   │   ├── session.py           # Interactive session manager
│   │   ├── simulator.py         # Command simulation engine
│   │   └── validator.py         # Exercise validation engine
│   ├── models/              # Pydantic data models
│   │   ├── course.py            # Course, Lesson, Exercise
│   │   ├── config.py            # AppConfig, LLMConfig
│   │   ├── enums.py             # Difficulty, LLMProvider, etc.
│   │   ├── progress.py          # Progress tracking
│   │   └── session.py           # Learning session
│   └── utils/               # Utilities
│       ├── llm_client.py        # Anthropic + OpenAI clients
│       ├── output.py            # Rich display helpers
│       └── serialization.py     # JSON serialization
├── tests/                   # 354 tests, 93% coverage
├── pyproject.toml           # Project configuration
├── README.md                # This file
└── CLAUDE.md                # Development guide

Contributing

This is currently a learning project. Contributions and suggestions are welcome!

License

MIT License - see LICENSE file for details

Learn More

For detailed architecture and development guidelines, see CLAUDE.md.