复用 iPolloWork 日程按固定视频号执行草稿准备、发布和运营数据同步，维持稳定操作标识与真实结果回读。
| Publisher | Devin-AXIS |
| Repository | iPolloWork |
| Skill name | wechat-channels-ops-dispatcher |
| Stars | 6.9K |
| Forks | 1.1K |
| Bundled files | Instructions only |
| Links |
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Everything the model needs lives in the instructions — no extra files to sync.
Published by Devin-AXIS on GitHub. Read the source before you install it.
Install the Wechat Channels Ops Dispatcher AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.
TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.
https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/wechat-channels-ops/skills/wechat-channels-ops-dispatcherAny agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.
git clone --depth 1 https://github.com/Devin-AXIS/iPolloWork.git /tmp/iPolloWork
mkdir -p .claude/skills
cp -r /tmp/iPolloWork/examples/plugin-packages/wechat-channels-ops/skills/wechat-channels-ops-dispatcher .claude/skills/wechat-channels-ops-dispatcherEnable Wechat Channels Ops Dispatcher 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.
AI skills are plain Markdown instructions rather than provider-specific code, so Wechat Channels Ops Dispatcher 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.
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.
This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.
先阅读 wechat-channels-ops-worker。使用宿主已有日程，不启动插件计时器。用户给出日程时间和目标后才创建日程。
复用 iPolloWork 日程按固定视频号执行草稿准备、发布和运营数据同步，维持稳定操作标识与真实结果回读。
Because you install it once and use it with any model. Wechat Channels Ops Dispatcher 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.
Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Devin-AXIS/iPolloWork/tree/main/examples/plugin-packages/wechat-channels-ops/skills/wechat-channels-ops-dispatcher. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.
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.
As many as you like. As long as a model supports skills, you can use Wechat Channels Ops Dispatcher with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.
It is published on GitHub by Devin-AXIS. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.
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.
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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