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Raspberry Pi AI Skill 🤖

Claude AI Skill for Professional Raspberry Pi Development with Edge AI Integration

Developed by Hayal

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📋 Overview

This Claude AI Skill supports the systematic development of robust, secure, and performant Raspberry Pi projects with a focus on Edge AI. It acts as a Senior Embedded Systems Architect and combines best practices from embedded systems, Linux administration, electrical engineering, and machine learning.

🎯 Key Features

• Project Workflow: Structured process from requirements analysis to deployment
• Hardware Integration: GPIO, I2C, SPI, cameras, sensors, HATs
• Edge AI: Ollama, Hailo-8L NPU, TensorFlow Lite
• Systematic Debugging: Isolation method, common pitfalls catalog, escalation paths
• Safety: Proactive validation of critical parameters (voltage, current, temperature)
• Pi 5 Support: Specific support for RP1 chip, PCIe Gen 3, Mini-CSI

🚀 Quick Start

Installation in Claude

1. Download the skill file:

   • Download: raspberry-pi-ai.skill

2. Upload to Claude:

   • Open claude.ai
   • Navigate to Settings → Skills
   • Click "Upload Skill"
   • Select raspberry-pi-ai.skill

3. Activate the skill:

   • The skill is now available in all conversations
   • Claude will automatically detect when it is relevant

Usage

The skill is automatically activated when you say things like:

"I want to set up a Raspberry Pi 5 with a camera and Ollama"
"My GPIO sensor isn't working"
"How do I integrate the Hailo-8L NPU?"
"Create a build plan for an AI camera"

📚 Documentation

Core Files

• SKILL.md – Main skill definition with workflows and checklists
• debugging-playbook.md – Systematic debugging framework

References

• hardware-specs.md – Raspberry Pi 4/5 specifications, GPIO pinouts, power budgets
• edge-ai.md – Ollama, Hailo-8L, TFLite setup and best practices
• component-catalog.md – Recommended components with suppliers

Templates

• plan-template.md – Project build plan template

🛠️ Features in Detail

Think-Hard Hierarchy

The skill adapts analysis depth to complexity:

Level	Trigger	Action
1	CLI commands, package management	Direct execution
2	Multi-component, sensor fusion	Create plan, check power/pins
3	Async, kernel, security	Analyze race conditions, memory leaks
4	NPU pipelines, quantization	Check tensor ops, bandwidth, thermal

Isolation Method

Core principle: Never debug hardware and software at the same time.

1. Test hardware in isolation → GPIO blink, I2C scan, camera test
2. Test software in isolation → Mock data, library imports
3. Test the interface → Permissions, device nodes, venv

Safety Checklists

Proactive validation of critical parameters:

• ✅ 3.3V GPIO tolerance (no 5V signals!)
• ✅ Power budget: Pi + peripherals < 80% PSU capacity
• ✅ Thermal management: Active cooler for Pi 5
• ✅ Inductive loads: Flyback diodes for motors/relays

🎓 Developed for Education

This skill was developed by Hayal to:

• Support teachers integrating Raspberry Pi in their classrooms
• Guide students in learning embedded systems systematically
• Make best practices from professional embedded development accessible
• Bridge the gap between theory and practice

📖 Examples

Example 1: GPIO LED Control

User: "Create a script that blinks an LED on GPIO17"

Claude (with skill):

# Pre-Flight Check:
# - GPIO17 is free (not used by HAT)
# - LED + resistor dimensioned (220Ω for 3.3V)

from gpiozero import LED
from time import sleep

led = LED(17)

try:
    while True:
        led.on()
        sleep(1)
        led.off()
        sleep(1)
except KeyboardInterrupt:
    led.close()

Example 2: Hailo-8L NPU Setup

User: "How do I install the Hailo-8L on the Pi 5?"

Claude (with skill):

1. Enable PCIe Gen 3 in /boot/firmware/config.txt
2. Mount active cooler (NPU + Pi 5 = ~15W additional load)
3. Install Hailo drivers
4. Set up thermal monitoring
5. Validate with hailortcli fw-control identify

→ Details in edge-ai.md

🔧 Development

Build Your Own .skill File

# Clone the repository
git clone https://github.com/malkreide/raspberry-pi-ai-skill.git
cd raspberry-pi-ai-skill

# Build the skill file
./scripts/build-skill.sh

# Output: raspberry-pi-ai.skill

Contributing

Contributions are welcome! Please:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-feature)
3. Commit your changes (git commit -m 'Add new feature')
4. Push to the branch (git push origin feature/new-feature)
5. Create a Pull Request

See CONTRIBUTING.md for details.

📊 Use Cases

Education

• Computer science classes (secondary level)
• STEM programs
• Raspberry Pi workshops
• Maker projects

Prototyping

• IoT proof-of-concepts
• Edge AI experiments
• Sensor networks
• Robotics projects

Production (Limited)

• Embedded dashboards
• Data acquisition
• Local AI inference
• Monitoring systems

⚠️ Known Limitations

• Pi 5 Mini-CSI: Camera cable incompatibility with Pi 4 cables
• PEP 668: Bookworm blocks system-wide pip installs → use venv
• RP1 Chip: Older HATs/libraries may be incompatible
• Thermal: Pi 5 requires active cooler for sustained loads

📜 License

MIT License - see LICENSE

🙏 Credits

• Development: Hayal Oezkan
• AI Framework: Anthropic Claude
• Hardware: Raspberry Pi Foundation
• Community: Raspberry Pi Forums, GitHub Contributors

📞 Contact & Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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Made with ❤️ in Zürich

LinkedIn • Documentation • Contributing