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Wxjava Module Selector

CommunityPopular
binarywang
wxjava-module-selector

根据微信公众号、小程序、微信支付、企业微信、开放平台、视频号或微信小店、腾讯企点和微信智能对话等业务场景,为用户选择合适的 WxJava Maven 模块、BOM 和示例入口。适用于用户询问“该用哪个模块”、依赖坐标、产品边界或单/多账号 Starter 选择时。

Overview

Publisherbinarywang
RepositoryWxJava
Skill namewxjava-module-selector
Stars
33.1K
Forks
9.1K
Bundled files
2
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by binarywang on GitHub. Read the source before you install it.

Installation

Install the Wxjava Module Selector AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/binarywang/WxJava.git /tmp/WxJava
mkdir -p .claude/skills
cp -r /tmp/WxJava/skills/wxjava-module-selector .claude/skills/wxjava-module-selector
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wxjava Module Selector in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Wxjava Module Selector on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Wxjava Module Selector is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

WxJava 模块选择

  1. 识别微信产品、服务端框架、是否多账号、是否包含支付或回调;信息不足时只询问必要问题。
  2. 读取 模块映射,给出一个主推荐,以及组合模块的理由。
  3. 先区分产品边界,再选择核心 SDK;仅当项目确实依赖框架自动配置时才额外推荐 Starter 或 Solon 插件。
  4. 优先推荐 BOM;给出准确的 groupIdartifactId、相应 Demo、Wiki 或仓库文档入口。
  5. 说明服务端 SDK 的边界:移动端登录、分享等能力仍需微信官方客户端 SDK。
  6. 不臆测版本号;建议以 Maven Central 或项目 README 的当前版本为准。

使用“场景 → 模块 → 集成选项 → 下一步”的简短结构。涉及多账号时说明单账号与 multi Starter 的区别;不要在示例中泄露凭据。支付和回调场景必须提醒用户验证通知 URL、验签与幂等处理。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Wxjava Module Selector AI skill do?

根据微信公众号、小程序、微信支付、企业微信、开放平台、视频号或微信小店、腾讯企点和微信智能对话等业务场景,为用户选择合适的 WxJava Maven 模块、BOM 和示例入口。适用于用户询问“该用哪个模块”、依赖坐标、产品边界或单/多账号 Starter 选择时。

Why use Wxjava Module Selector on TypingMind?

Because you install it once and use it with any model. Wxjava Module Selector is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Wxjava Module Selector in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/binarywang/WxJava/tree/develop/skills/wxjava-module-selector. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Wxjava Module Selector?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Wxjava Module Selector?

As many as you like. As long as a model supports skills, you can use Wxjava Module Selector with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Wxjava Module Selector AI skill free?

Yes. It is published on GitHub by binarywang under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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