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Lark Workflow Meeting Notes

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liangdabiao
lark-workflow-meeting-notes

智能会议纪要分发:获取已完成会议的纪要内容,根据参会状态智能分发——完整纪要发给参会者,摘要版发给缺席者。当用户需要'分发会议纪要'、'发送纪要'、'会议总结分发'、'会后通知'、'纪要发给没参加的人'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-meeting-notes
Stars
68
Forks
10
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Lark Workflow Meeting Notes 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.
    https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-meeting-notes
  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/liangdabiao/lark-workflow-feishu-cli.git /tmp/lark-workflow-feishu-cli
mkdir -p .claude/skills
cp -r /tmp/lark-workflow-feishu-cli/.claude/skills/lark-workflow-meeting-notes .claude/skills/lark-workflow-meeting-notes
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Meeting Notes 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 Lark Workflow Meeting Notes 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 Lark Workflow Meeting Notes 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.

智能会议纪要分发工作流

CRITICAL — 开始前 MUST 先用 Read 工具读取 ../lark-shared/SKILL.md,其中包含认证、权限处理

适用场景

  • "会议结束了,帮我整理纪要发给大家" / "分发会议纪要"
  • "把今天的会议记录发给没参加的人" / "会议总结分发"
  • "帮我发一下刚才那个会的纪要" / "发送纪要"
  • "会后通知参会人" / "会后通知"

前置条件

仅支持 user 身份。执行前确保已授权:

bash
lark-cli auth login --domain calendar,vc,docs,im

工作流

{event_id 或 会议名称/时间}
  │
  ├── calendar events get ──────────► 获取会议详情 + 参会人列表
  │
  ├── [有纪要] docs +fetch ─────────► 读取纪要内容
  │     或 vc 获取纪要文档
  │
  ├── AI 区分参会人 ────────────────► 出席者 / 缺席者
  │
  ├── AI 生成摘要版本 ──────────────► 缺席者版(摘要+决策+待办)
  │
  ├── im +messages-send ────────────► 完整纪要 → 出席者
  │
  └── im +messages-send ────────────► 摘要 + 行动项 → 缺席者

Step 1: 获取会议详情

如果用户提供了 event_id,直接查询;否则先通过日程列表查找:

bash
# 直接查询
lark-cli calendar events get --params '{"calendar_id":"primary","event_id":"<event_id>","need_attendee":true}' --as user

从返回结果中提取:

  • summary — 会议主题
  • start_time / end_time — 会议时间
  • 参会人列表和 RSVP 状态

获取参会人状态

bash
lark-cli calendar event.attendees list \
  --params '{"calendar_id":"primary","event_id":"<event_id>","page_size":50}' \
  --as user

根据 rsvp_status 分类:

  • accept → 出席者
  • tentative → 待定(按缺席处理)
  • decline / needs_action → 缺席者

Step 2: 获取会议纪要

方式一:从飞书文档获取

如果会议关联了纪要文档,使用 docs +fetch:

bash
lark-cli docs +fetch --doc "<doc_url 或 token>" --as user

方式二:从妙记/视频会议获取

如果会议有视频会议记录,可以通过 vc 获取纪要:

bash
# 查询会议记录
lark-cli vc --help  # 查看可用命令

方式三:用户提供纪要内容

如果用户直接提供了纪要文本或要求 AI 根据会议主题生成纪要草稿,直接使用用户提供的内容。

注意:如果会议没有纪要文档,告知用户"该会议暂无纪要文档",并询问是否需要根据会议主题生成纪要草稿。

Step 3: AI 整理纪要内容

完整版(给出席者)

保留纪要的全部内容,整理为结构化格式。

摘要版(给缺席者)

AI 从完整纪要中提取关键信息:

markdown
## {会议主题} — 会议摘要

**时间**:{日期时间}
**参会人**:{N} 人出席,{N} 人缺席

### 关键决策
- {决策1}
- {决策2}

### 待办事项
| 事项 | 负责人 | 截止日期 |
|------|--------|---------|

### 重要讨论
- {讨论要点1}
- {讨论要点2}

> 完整纪要已上传至飞书文档:{链接}

Step 4: 分发纪要

给出席者(完整版)

bash
lark-cli im +messages-send \
  --user-id "<attendee_open_id>" \
  --markdown "{完整纪要内容}" \
  --as bot

给缺席者(摘要版)

bash
lark-cli im +messages-send \
  --user-id "<absentee_open_id>" \
  --markdown "{摘要版内容}" \
  --as bot

注意:发送消息时每次间隔 1-2 秒,避免触发频率限制。如果参会人数超过 20 人,分批发送。

降级策略

场景降级方案
找不到会议提示用户提供 event_id 或会议时间
无纪要文档询问用户是否需要生成纪要草稿
参会人信息获取失败直接发送给发起人,让发起人手动转发
消息发送频率限制分批发送(每批 10 人,间隔 2 秒)
某人发送失败记录失败列表,最后汇总告知用户

权限表

命令所需 scope
calendar events getcalendar:calendar:readonly, calendar:event:readonly
calendar event.attendees listcalendar:calendar:readonly
docs +fetchdocx:document:readonly
im +messages-sendim:message

参考

Frequently asked questions

What does the Lark Workflow Meeting Notes AI skill do?

智能会议纪要分发:获取已完成会议的纪要内容,根据参会状态智能分发——完整纪要发给参会者,摘要版发给缺席者。当用户需要'分发会议纪要'、'发送纪要'、'会议总结分发'、'会后通知'、'纪要发给没参加的人'时使用。

Why use Lark Workflow Meeting Notes on TypingMind?

Because you install it once and use it with any model. Lark Workflow Meeting Notes 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 Lark Workflow Meeting Notes in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-meeting-notes. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Meeting Notes?

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 Lark Workflow Meeting Notes?

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

Is the Lark Workflow Meeting Notes AI skill free?

It is published on GitHub by liangdabiao. Check the repository for licensing terms. 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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