Edge Deployment Skill

SkillCloud & infra

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

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the Edge Deployment Skill skill

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/robotics-simulation/skills/edge-deployment/SKILL.md and read by ahel’s review.

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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: edge-deployment-skill
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter