Wechat Moments

Organization

OpenClaudia

wechat-moments

Rank and summarize the user's own WeChat Moments (朋友圈) feed so real life events and genuine information rise above promotion and small talk, weighted by how much the user actually talks to each author. Use when the user says 'summarize my Moments', 'read my 朋友圈', 'what did I miss on Moments', 'rank my Moments', or 'check my WeChat Moments'.

Overview

Publisher	OpenClaudia
Repository	`openclaudia-skills`
Skill name	`wechat-moments`
Stars	714
Forks	60
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by OpenClaudia on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/wechat-moments
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.

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/OpenClaudia/openclaudia-skills.git /tmp/openclaudia-skills
mkdir -p .claude/skills
cp -r /tmp/openclaudia-skills/skills/wechat-moments .claude/skills/wechat-moments

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Wechat Moments 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 Wechat Moments 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 Wechat Moments 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.

WeChat Moments digest

Turns a WeChat Moments feed into a short ranked digest: what deserves a reply, what is genuinely informative, and what is marketing wearing a personal voice.

The scripts live in a separate repository, not here, because they depend on the WeChat desktop client rather than any marketing API:

https://github.com/OpenClaudia/wechat-moments

bash
git clone https://github.com/OpenClaudia/wechat-moments.git

Read that repo's README before running anything. It documents the setup, the limits, and the risks — this skill is the ranking method, not the plumbing.

Before you touch it

It does not decrypt anything and never handles keys. It reads a plaintext database that a separate WeChat export tool has already produced. If the user has no export yet, that is step one and it is not this skill's job — the repo lists existing tools for it.
Moments do not sync in the background. WeChat fetches the timeline only while the Moments window is open. Without a sync the export contains only what the user already scrolled past, so the digest is a replay of what they have already seen. Say so rather than presenting stale posts as today's.
Tell the user the limits before setting this up on a schedule. It needs WeChat running and logged in, it only stays complete while the machine is awake (sleep gaps are permanently unrecoverable), Tencent may break or patch it at any time, automating the client may be noticed and may carry account risk, and nothing is guaranteed.
Never post, like, comment, or message on the user's behalf. Reading and writing files is the whole remit.

Ranking

This is the part worth carrying to any feed, not just WeChat.

Score = event weight × relationship weight. Do it yourself from the structured records; do not write a scoring script. The point is judgement — "her mother is in hospital" outranks "went to Kyoto".

Event weight (0-10). Birth, death, serious illness, wedding, divorce, emigration at the top. New job, funding, graduation, a home, a pregnancy just below. Travel and opinions in the middle. Food, gym, weather, scenery at the bottom. Promotion and recruiting score zero.

Insight vs. announcement decides most of the digest, because feeds full of founders and creators are full of engagement bait. Real information has at least one of: a number you could check, a mechanism (why it works), a cost the author actually paid, or a changed mind. Adjectives (突破 / 颠覆 / groundbreaking), a milestone with no mechanism, an invitation, an upvote request, or a takeaway generic enough to predate the event are announcements. Prestige is not evidence — a famous venue is still promotion when the payload is one generic sentence. A tiny post can be real insight.

Relationship weight — lead with how much the user SENT. Not total volume: a marketer who broadcasts dozens of pitches and never gets a reply scores identically to a close friend. A reply is a choice; an inbound blast is not. Never-messaged contacts get filtered hard whatever they posted — expect them to be about a third of any day's feed.

A close friend's small post usually beats a stranger's big news, because only one of them is a relationship the user can act on. Ranking on content alone inverts this every time.

Time since last contact is its own signal. Many messages sent but silent for a long time is a lapsed close friend, and a life event from one of those is the most actionable thing a digest can surface.

Promotion wears a personal voice. A discount code, a signup link, an invite-only pitch: the poster is selling, however casual the phrasing.

Urgency is a modifier, never a reason. Never open a digest with a dated section — that structure promotes whatever has a deadline, and deadlines are what promotions have. An expiring ad is still an ad.

Output

Ten items is plenty; fewer is often right, and zero worth reporting is a legitimate result — say so rather than padding. Keep strong ties and weak-tie-but-useful content in separate, clearly labelled sections, so interesting content from a stranger never looks like something to act on. Show the relationship numbers per entry so the ranking is auditable and the user can correct it.

Summarize Chinese posts in Chinese and English posts in English. Say what a post is; never repost it verbatim.

Frequently asked questions

What does the Wechat Moments AI skill do?

Why use Wechat Moments on TypingMind?

Because you install it once and use it with any model. Wechat Moments 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 Wechat Moments in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/wechat-moments. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Wechat Moments?

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 Wechat Moments?

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

Is the Wechat Moments AI skill free?

Yes. It is published on GitHub by OpenClaudia under the MIT 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

WeChat Moments digest

Before you touch it

Ranking

Output

Frequently asked questions

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