wendy

SKILL开发
开发 未标注权限 许可证不明确

Expert guidance on building and deploying apps to WendyOS edge devices. Use when developers mention: (1) Wendy or WendyOS, (2) wendy CLI commands, (3) wendy.json or entitlements, (4) deploying apps to edge devices, (5) remote debugging Swift on ARM64, (6) NVIDIA Jetson or Raspberry Pi apps, (7) cross-compiling Swift for ARM64.

一句话选型结论

按资料字段整理

wendy是开发类的 Agent Skill,安装方式:GitHub 仓库,权限级别未标注,许可证不明确,没有现成安装命令,需按说明手动放置 SKILL.md。技能本质是给模型读的说明书,效果取决于模型与客户端;装完先用一个小任务验证,再放进正式流程。

分类开发 安装方式GitHub 仓库 权限级别未标注权限 许可证许可证不明确
累计安装 未标注 本周 未标注
GitHub 星标 52 仓库 claude-skills
下载量 未标注 技能市场下载数
权限级别 未标注权限 安装前请确认风险范围
许可证 许可证不明确 未说明

安装

GitHub 仓库
在支持 Skills 的客户端里执行,装完问一句「你有哪些技能」验证
  1. 1
    确认客户端支持

    确认你的客户端支持 Skills(Claude、Codex、Cursor 等),并升级到较新的版本。

  2. 2
    拿到技能文件

    按安装说明或仓库 README,把 SKILL.md 放到客户端的 skills 目录(一般一个技能一个文件夹)。

  3. 3
    刷新技能列表

    重新打开客户端,或刷新技能列表,让新放进来的技能被加载。

  4. 4
    验证是否生效

    问一句「你有哪些技能」,确认列表里出现它,再拿一个小任务试跑。

技能档案

开发
字段来自技能仓库与公开索引,中文说明由本站标注
作者 / 维护方wendylabsinc仓库 claude-skills · 版本 未标注
宿主客户端未标注技能可被这些客户端加载
安装方式GitHub 仓库安装包 wendylabsinc/claude-skills
权限级别未标注权限安装前请确认风险范围
许可证未说明许可证不明确,商用前请自行确认条款
仓库可用性available技能仓库当前是否可访问
最近核验未标注上游最后核验时间 · 数据更新 未标注
正文体量5.8 KBSKILL.md 原文大小,越大说明说明书写得越细

典型使用场景

3 类
按分类整理的常见用法,具体以 SKILL.md 为准
写代码与改 bug

按需求补代码、定位并修掉报错。

读仓库与生成改动

让模型读懂项目结构,再给出可落地的改动。

跑测试与排查故障

按现象排查问题,补测试用例并复跑。

选择建议

装之前先看这几条
按条目字段给出的判断,不替你决定用不用

值得选的地方

  • 基础信息齐全,可以先按说明装一个试试。

先想清楚的地方

  • 没有标注权限级别,实际能做什么要装好后确认。
  • 许可证不明确,商用或二次分发前先到仓库确认条款。
  • 没有现成安装命令,要按安装说明或仓库 README 手动放置。
  • 没有做站内核验,内容以作者仓库为准。
  • 技能只是说明书,实际效果取决于模型与客户端,装完先用小任务试一遍。

更适合

  • 需要在开发场景里给模型补一套做法的人。
  • 愿意先读一遍 SKILL.md、确认它会让模型做什么的人。

不太适合

  • 对许可证要求很严、必须明确可商用的人。
  • 只想装完就自动变强、不打算看说明书的人。

同类对比

6 个同类
同分类里挑最接近的几条,字段以各自详情页为准
技能权限级别安装方式累计安装宿主客户端安装命令
wendy当前 未标注权限 GitHub 仓库 未标注 未标注 无
技能审查员 浏览器控制 ClawHub 市场 877.9 万 OpenClaw 有
Vercel React 最佳实践 浏览器控制 GitHub 仓库 849.1 万 Codex、Claude、Cursor 有
GitHub 代码协作 联网访问 ClawHub 市场 763.3 万 OpenClaw 有
网页设计指南 联网访问 GitHub 仓库 668.6 万 Codex、Claude、Cursor 有
本体论 只读 ClawHub 市场 663.7 万 OpenClaw 有

SKILL.md 全文

5.8 KB
模型实际读到的说明书原文,来自技能仓库

WendyOS

WendyOS is an Embedded Linux operating system for edge computing. It supports:
- NVIDIA Jetson devices (production with OTA updates)
- Raspberry Pi 4/5 (edge devices)
- ARM64/AMD64 VMs (development)

Learning About Wendy

Before helping with Wendy commands, run this to learn all available commands:

wendy --experimental-dump-help

This outputs a JSON structure with all commands, flags, and documentation.

Whenever you invoke a wendy command, use the JSON structure options to provide structured JSON output. This will also prevent interactive dialogs and errors. Use --json or -j to provide JSON output.

Common Tasks

  • Run an app: wendy run
  • Create a new project: wendy init
  • Discover devices: wendy discover
  • Update agent: wendy device update
  • Configure WiFi: wendy device wifi connect
  • Install WendyOS on an external drive: wendy os install
  • Set a device as default using wendy device set-default

wendy init — Create a New Wendy Lite Project

Creates a new Wendy Lite project with the required scaffolding:

wendy init

This sets up a new project directory with a wendy.json configuration file and the necessary structure for building and deploying a Wendy Lite app.

wendy run — Run a Wendy Lite Project

Builds, uploads, and runs a Wendy Lite project on a connected device:

wendy run

This command handles the full development cycle: compiling the app, transferring the binary to the device, and starting execution. Use --verbose for detailed build output.

wendy device wifi connect — Set Up WiFi

Configures WiFi credentials on a connected device:

wendy device wifi connect

This sends WiFi SSID and password to the device so it can connect to the local network. The device must be reachable over USB or an existing connection first.

Setup and Configuration

Wendy CLI connects to a device over gRPC (TCP) port 50051. If Wendy CLI is not installed yet, you can use brew install wendy to install it.

Devices are discovered over USB or LAN. If a device is not found, ask the user to check the connection or to connect it over USB.
If a device is not yet installed, use wendy os install to install the OS to an external drive. For NVIDIA Jetson devices, the OS is commonly installed to NVMe.

Development

WendyOS is a Linux-based containerized operating system. It uses Linux containers to run your apps.

WendyOS uses Swift.org as its flagship language. This uses Swift Package Manager and the Swift Container Plugin to build and run your app. Wendy CLI will cross compile Swift for you.

Other programming languages are supported, but require the use of a Dockerfile to build your app.

Entitlements

WendyOS uses an entitlement system, managed through wendy.json, to manage permissions for your app. This reflects how your container will be set up on the device.

See references/wendy.json.md for detailed entitlement configuration.

Quick Start

  1. Create a new Swift project or navigate to an existing one
  2. Initialize wendy.json: wendy project init
  3. Add required entitlements (e.g., for a web server): wendy project entitlements add network --mode host
  4. Run on device: wendy run

Common Entitlements

Entitlement Use Case
network (host mode) Web servers, HTTP APIs, incoming connections
gpu ML inference, computer vision (Jetson only)
video Camera access, video capture
audio Microphone, speakers
bluetooth BLE devices, Bluetooth communication

Remote Debugging

WendyOS provides built-in support for remote debugging Swift apps. Use wendy run --debug to include and launch a debugging session.
This exposes a GDB server on port 4242.

Connecting from VS Code

  1. Run wendy run --debug
  2. In VS Code, use the CodeLLDB extension
  3. Connect to <device-ip>:4242

Observability

WendyOS runs a local OpenTelemetry collector on each device. Apps should report telemetry (logs, metrics, traces) to this local collector.

Configuration

Use HTTP protocol (not gRPC) for OTel exports:

import OTel

var config = OTel.Configuration.default
config.traces.otlpExporter.protocol = .httpProtobuf
config.traces.otlpExporter.endpoint = "http://localhost:4318"
config.metrics.otlpExporter.protocol = .httpProtobuf
config.metrics.otlpExporter.endpoint = "http://localhost:4318"
config.logs.otlpExporter.protocol = .httpProtobuf
config.logs.otlpExporter.endpoint = "http://localhost:4318"

let observability = try OTel.bootstrap(configuration: config)

Or via environment variables:

OTEL_EXPORTER_OTLP_PROTOCOL=http/protobuf
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4318

The local collector handles forwarding telemetry to your backend infrastructure.

Troubleshooting

Problem Solution
Device not found Check USB/LAN connection, run wendy discover
Network access denied Add network entitlement with host mode
GPU not detected Add gpu entitlement (Jetson only)
Camera not found Add video entitlement, verify camera at /dev/video0
Build fails Check Swift version compatibility, try wendy run --verbose

Reference Files

Load these files as needed for specific topics:

  • references/wendy.json.md - App configuration, entitlements (network, gpu, video, audio, bluetooth), common configurations, CLI commands

Further Reading

WendyOS documentation at https://wendy.sh/docs/

常见问题

5 问
答案由本站依据条目字段整理
wendy怎么安装?

没有现成安装命令:按安装说明或仓库 README,把 SKILL.md 放进客户端的 skills 目录(一般一个技能一个文件夹)。

wendy支持哪些客户端?

资料没有标注宿主客户端,但只要客户端支持 Skills(Claude、Codex、Cursor 等),一般都能加载。

wendy权限高吗,安全吗?

权限级别为未标注权限。安装前先看它会读取或写入哪些内容。

wendy收费吗?

本站不代收费用,也没有标注具体价格。是否收费取决于技能本身和所用的模型或客户端,正式使用前按作者说明确认。

wendy用什么许可证?

许可证标注为 未说明(许可证不明确)。商用或二次分发前请到仓库确认条款原文。

热门专题

按场景挑技能
全部专题 →

技能内容以作者仓库为准;分类、权限级别与许可证状态的中文说明为中文整理,最近核验 未标注。