Edge Deployment Skill
ML model optimization and deployment on robot edge devices (Jetson, embedded)
npx skills add a5c-ai/babysitter --skill edge-deployment --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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
- Overview
- Capabilities
- Target Processes
- Dependencies
- Usage Context
- Output Artifacts
What does the Edge Deployment Skill skill do?
ML model optimization and deployment on robot edge devices (Jetson, embedded)
How do I install it?
Run `npx skills add a5c-ai/babysitter --skill edge-deployment --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From a5c-ai/babysitter, a repository with 1,642 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.