goose-mentor-mode 0.1.2

AI-powered mentor extension for Goose that transforms automation into guided learning

Goose Mentor Mode 🎓

AI-powered mentor extension for Goose that transforms development assistance from automation into guided learning experiences.

🚀 Features

• Adaptive Learning Assistance: Four assistance levels (GUIDED, EXPLAINED, ASSISTED, AUTOMATED)
• Socratic Questioning: Helps users discover solutions through guided questions
• Learning Opportunity Detection: Automatically identifies educational moments
• Progress Tracking: Monitors learning progress and provides recommendations
• Environment Configuration: Easy setup through environment variables
• Goose Integration: Seamless integration with Goose AI assistant

🎉 Now Available on PyPI!

Goose Mentor Mode is officially published and available to the entire Python community! Install it with a single command and start transforming your AI assistance from automation to education.

📦 Installation

Goose Mentor Mode is a Goose extension that integrates seamlessly with Goose Desktop. There are two installation methods:

🚀 Quick Install via PyPI (Recommended)

# Install the package
pip install goose-mentor-mode

# Or with uv
uv add goose-mentor-mode

📦 PyPI Package

• PyPI: https://pypi.org/project/goose-mentor-mode/
• Latest Version:

🛠️ Development Installation

# Clone and install for development
git clone https://github.com/joeeuston-dev/goose-mentor-mode.git
cd goose-mentor-mode
uv sync

# Or with pip in development mode
pip install -e .

⚙️ Configuration

Goose Desktop Integration

After installing the package, configure it in Goose Desktop:

Step 1: Install the Package

pip install goose-mentor-mode

Step 2: Configure in Goose Desktop

Method 1: Through Goose Desktop UI

1. Open Goose Desktop
2. Go to Settings → Profiles
3. Select your profile or create a new one
4. Add mentor to the Toolkits list
5. Optionally configure environment variables for customization

Method 2: Direct Profile Configuration

Add to your Goose profile configuration:

toolkits:
  - name: mentor
    package: goose-mentor-mode

Step 3: Environment Configuration (Optional)

Customize behavior using environment variables:

# Core Configuration
DEFAULT_ASSISTANCE_LEVEL=guided          # guided|explained|assisted|automated
LEARNING_PHASE=skill_building           # onboarding|skill_building|production
TIMELINE_PRESSURE=low                   # low|medium|high
ENABLE_VALIDATION_CHECKPOINTS=true     # Enable learning validation
MAX_GUIDANCE_DEPTH=3                    # Depth of Socratic questioning
DEVELOPER_EXPERIENCE_MONTHS=6           # Developer experience level

Environment Variable Configuration in Goose Desktop:

1. Go to Settings → Profiles → [Your Profile]
2. Add environment variables in the Environment section
3. Save and restart Goose Desktop

📖 For detailed usage examples and scenarios, see USAGE_EXAMPLES.md

🎯 For complete Goose Desktop setup instructions, see GOOSE_DESKTOP_CONFIG.md

🎯 Assistance Levels

🧭 GUIDED Mode

• Purpose: Learning through discovery
• Approach: Socratic questioning and guided exploration
• Best For: New concepts, skill building, deep understanding
• Example: "What do you think JWT stands for? How might stateless authentication work?"

📚 EXPLAINED Mode

• Purpose: Education with solutions
• Approach: Detailed explanations with implementation
• Best For: Time-sensitive tasks with learning value
• Example: "Here's how JWT works... [detailed explanation] + working code"

🤝 ASSISTED Mode

• Purpose: Quick help with learning opportunities
• Approach: Direct help with educational context
• Best For: Experienced developers needing quick assistance
• Example: "Use this JWT library. Key security considerations: [brief points]"

⚡ AUTOMATED Mode

• Purpose: Direct task completion
• Approach: Efficient solutions without educational overhead
• Best For: Production pressure, repeated tasks
• Example: "Here's the complete JWT implementation."

🛠️ Tools

mentor_analyze_request

Analyzes user requests for learning opportunities and recommends assistance levels.

toolkit.mentor_analyze_request(
    user_request="How do I implement JWT authentication?",
    context={"experience_months": 6, "timeline_pressure": "low"}
)

mentor_learning_check

Validates understanding through Socratic questioning.

toolkit.mentor_learning_check(
    concept="JWT Authentication",
    user_explanation="JWT is a token that contains user information",
    expected_understanding=["stateless", "secure", "token-based"]
)

mentor_track_progress

Tracks learning progress and provides recommendations.

toolkit.mentor_track_progress(
    activity="Implementing JWT authentication",
    success_indicators={"task_completed": True, "time_spent": 30}
)

mentor_suggest_assistance_level

Suggests optimal assistance level for given context.

toolkit.mentor_suggest_assistance_level(
    user_request="I need help with AWS Lambda",
    context={"experience_months": 6, "timeline_pressure": "medium"}
)

🎓 Educational Philosophy

Mentor Mode transforms AI assistance from automation to education:

• Discovery Over Delivery: Help users understand why, not just how
• Adaptive Learning: Adjusts approach based on experience and context
• Progressive Complexity: Builds understanding layer by layer
• Retention Focus: Emphasizes learning that sticks

🔧 Developer Profiles

New Developer (0-6 months)

DEFAULT_ASSISTANCE_LEVEL=guided
LEARNING_PHASE=onboarding
TIMELINE_PRESSURE=low
ENABLE_VALIDATION_CHECKPOINTS=true

Developing Skills (6-24 months)

DEFAULT_ASSISTANCE_LEVEL=explained
LEARNING_PHASE=skill_building
TIMELINE_PRESSURE=medium
ENABLE_VALIDATION_CHECKPOINTS=true

Experienced Developer (24+ months)

DEFAULT_ASSISTANCE_LEVEL=assisted
LEARNING_PHASE=production
TIMELINE_PRESSURE=medium
ENABLE_VALIDATION_CHECKPOINTS=false

🧪 Testing

# Run all tests
uv run pytest

# Run with coverage
uv run pytest --cov=goose_mentor_mode

# Run specific test
uv run pytest tests/test_mentor_toolkit.py::TestMentorToolkit::test_mentor_analyze_request

🤝 Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Make your changes
4. Add tests for new functionality
5. Run tests (uv run pytest)
6. Commit your changes (git commit -m 'Add amazing feature')
7. Push to the branch (git push origin feature/amazing-feature)
8. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built for the Goose AI Assistant
• Inspired by Socratic teaching methods
• Designed for developers who value learning

📞 Support

• Issues: GitHub Issues
• Documentation: GitHub Wiki
• Discussions: GitHub Discussions

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Transform your AI assistance from automation to education with Goose Mentor Mode! 🎓✨