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WeChat Messaging via weixin-agent-cli

Automate WeChat message polling and replies via weixin-agent-cli JSON/NDJSON.

7|Updated Mar 29, 2026
Search Tags:#messaging#polling#wechat#json output#context token#media sending#ai agent automation
One-click install
npx skills add https://github.com/Ginurx/weixin-agent-cli --skill wechat-messaging-via-weixin-agent-cli
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: WeChat Messaging via weixin-agent-cli
Source: https://github.com/Ginurx/weixin-agent-cli/tree/main
Command: npx skills add https://github.com/Ginurx/weixin-agent-cli --skill wechat-messaging-via-weixin-agent-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill helps you reliably automate WeChat messaging for AI agents by turning the WeChat send/receive flow into predictable JSON outputs you can feed into your agent loop.

Core Features & Use Cases

  • Poll for incoming messages (single or continuous): Retrieve completed user messages as JSON, or stream them as NDJSON in watch mode.
  • Reply with context-safe sending: Automatically cache and reuse the required context token by polling before sending.
  • Send rich content: Send text, media (image/video/file) with MIME routing by extension, and typing indicators to improve conversational UX.
  • Multi-account support: Operate with one or many logged-in bot accounts by selecting the appropriate account ID.

Use case: build an agent that continuously listens to a customer’s chat, processes each incoming message, and replies with text and optionally an attached image/document using a single terminal-friendly interface.

Quick Start

Use the Skill to poll for messages, then send a reply text to the sender using the received context.

Frequently Asked Questions about WeChat Messaging via weixin-agent-cli

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate WeChat messaging for AI agents using JSON output?▼

Automating WeChat messaging for AI agents involves polling for incoming messages and replying with structured JSON outputs. This skill uses a JSON/NDJSON interface to feed chatbot loops and send text or media deterministically.

How do I poll WeChat messages continuously for real-time chatbot processing?▼

Polling WeChat messages continuously for real-time chatbot processing is done using watch mode. This streams completed user messages as NDJSON, allowing your agent loop to process incoming chats as discrete structured events.

Why do I need to poll for messages before sending a WeChat reply?▼

Polling before sending a WeChat reply is required to cache the mandatory context token. This context-safe sending mechanism ensures your automated agent reuses the correct session token to deliver text or media successfully.

Can I send media files like images and videos through WeChat agent automation?▼

Sending media files like images and videos through WeChat agent automation is supported via extension-routed MIME handling. You can send text, images, videos, and files to specific user IDs alongside typing indicators.

Does WeChat agent automation support multi-account selection?▼

WeChat agent automation supports multi-account selection by targeting specific logged-in bot account IDs. This allows you to operate across one or many accounts within a single terminal-friendly interface.

What is the best way to structure WeChat chat data for deterministic agent consumption?▼

The best way to structure WeChat chat data for deterministic agent consumption is using a JSON-first interface. This approach transforms the send and receive flow into predictable JSON or NDJSON suitable for automated processing.

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