StanleyChanH

MaixPy Skill — AI skill for Claude Code

AI community

Claude Code skill for MaixPy development on MaixCAM edge AI devices 用于 MaixCAM 边缘 AI 设备 MaixPy 开发的 Claude Code 技能包.

How to install MaixPy Skill

This entry records only its repository, not the path inside it, so there is no exact command to give. Open StanleyChanH/MaixPy-skill and copy the folder into ~/.claude/skills/, or the file into ~/.claude/agents/.

What MaixPy Skill does

Claude Code skill for MaixPy development on MaixCAM edge AI devices 用于 MaixCAM 边缘 AI 设备 MaixPy 开发的 Claude Code 技能包.

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README

MaixPy Development Skill

[![License](https://img.shields.io/badge/license-MIT-blue.svg)](LICENSE) [![MaixPy](https://img.shields.io/badge/MaixPy-v4-green.svg)](https://github.com/sipeed/MaixPy) [![Claude Code](https://img.shields.io/badge/Claude%20Code-Skill-orange.svg)](https://claude.ai/code)

**[English](README.md)** | **[简体中文](README_CN.md)**

A comprehensive Claude Code skill for developing Python applications on Sipeed MaixCAM, MaixCAM-Pro, and MaixCAM2 edge AI devices using MaixPy v4.

Features

AI Vision: YOLO detection, segmentation, pose estimation, classification, face recognition, OCR
Image Processing: Blob detection, edge detection, QR/barcode scanning, line tracking
Object Tracking: ByteTracker, counting, trajectory visualization
Peripherals: Camera, display, UART, I2C, SPI, GPIO, PWM, ADC, USB HID
Network: WiFi, HTTP streaming, MQTT, WebSocket, RTSP/RTMP
Audio: Playback, recording, TTS, ASR
LLM/VLM: Qwen, DeepSeek, InternVL (MaixCAM2 only)
Advanced: OpenCV integration, video encoding/decoding, self-learning classifier

Installation

Method 1: Direct Download

Download `maixpy-dev.skill` and place it in your Claude Code skills directory:

~/.claude/skills/

Method 2: Clone Repository

git clone https://github.com/StanleyChanH/MaixPy-skill.git

Quick Start

from maix import camera, display, image, nn, app

detector = nn.YOLOv8(model="/root/models/yolov8n.mud", dual_buff=True)
cam = camera.Camera(detector.input_width(), detector.input_height(), detector.input_format())
disp = display.Display()

while not app.need_exit():
    img = cam.read()
    objs = detector.detect(img, conf_th=0.5, iou_th=0.45)
    for obj in objs:
        img.draw_rect(obj.x, obj.y, obj.w, obj.h, color=image.COLOR_RED)
    disp.show(img)