# Edge Deployment Skill

> ML model optimization and deployment on robot edge devices (Jetson, embedded)

- Skill: `a5c-ai/edge-deployment-skill` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add a5c-ai/edge-deployment-skill`
- Raw SKILL.md: https://api.skillmd.com/api/skills/a5c-ai/edge-deployment-skill/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: a5c-ai (https://skillmd.com/u/a5c-ai)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/a5c-ai/edge-deployment-skill

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# 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