a5c-ai/Edge Deployment Skill
library/specializations/robotics-simulation/skills/edge-deployment/SKILL.md
ML model optimization and deployment on robot edge devices (Jetson, embedded)
$ install --global
skillsauthnpx skillsauth add a5c-ai/babysitter Edge Deployment SkillInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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SKILL.md
- name:
- Edge Deployment Skill
- description:
- ML model optimization and deployment on robot edge devices (Jetson, embedded)
- slug:
- edge-deployment
- category:
- Deployment
Edge Deployment Skill
Overview
Expert skill for optimizing and deploying machine learning models on robot edge devices including NVIDIA Jetson and embedded systems.
Capabilities
- Configure TensorRT optimization for NVIDIA Jetson
- Set up ONNX model conversion and optimization
- Implement INT8 and FP16 quantization
- Configure DeepStream for video analytics
- Set up CUDA graph optimization
- Implement model pruning and distillation
- Configure DLA (Deep Learning Accelerator) deployment
- Set up multi-stream inference
- Implement ROS2 inference nodes
- Profile and benchmark on target hardware
Target Processes
- nn-model-optimization.js
- object-detection-pipeline.js
- rl-robot-control.js
- field-testing-validation.js
Dependencies
- TensorRT
- ONNX Runtime
- NVIDIA Jetson SDK
- DeepStream
Usage Context
This skill is invoked when processes require deploying ML models on edge devices with optimized inference performance.
Output Artifacts
- TensorRT engine files
- ONNX optimized models
- Quantization configurations
- DeepStream pipeline configs
- Inference benchmark reports
- ROS2 inference node implementations
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