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Lark Vc

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liangdabiao
lark-vc

飞书视频会议:查询会议记录、获取会议纪要产物(总结、待办、章节、逐字稿)。1. 查询已经结束的会议数量或详情时使用本技能(如昨天 | 上周 | 今天已经开过的会议等场景),查询未开始的会议日程使用 lark-calendar 技能。2. 支持通过关键词、时间范围、组织者、参与者、会议室等筛选条件搜索会议记录。3. 获取或整理会议纪要时使用本技能。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-vc
Stars
68
Forks
10
Bundled files
2
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 liangdabiao on GitHub. Read the source before you install it.

Installation

Install the Lark Vc 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-vc
  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-vc .claude/skills/lark-vc
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Vc 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 Vc 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 Vc 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.

vc (v1)

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

核心概念

  • 视频会议(Meeting):飞书视频会议实例,通过 meeting_id 标识。
  • 会议记录(Meeting Record):视频会议结束后生成的记录,支持通过关键词、时间段、参会人、组织者、会议室等筛选条件搜索会议室。
  • 会议纪要(Note):视频会议结束后生成的结构化文档,包含纪要文档(包含总结、待办、章节)和逐字稿文档。
  • 妙记(Minutes):来源于飞书视频会议的录制产物或用户上传的音视频文件,支持视频/音频的转写和会议纪要,通过 minute_token 标识。
  • 纪要文档(MainDoc):会议纪要的主文档,包含 AI 生成的总结和待办。
  • 逐字稿(VerbatimDoc):会议的逐句文字记录,包含说话人和时间戳。

核心场景

1. 搜索会议记录

  1. 仅支持搜索已结束的会议,对于还未开始的未来会议,需要使用 lark-calendar 技能。
  2. 仅支持使用关键词、时间段、参会人、组织者、会议室等筛选条件搜索会议记录,对于不支持的筛选条件,需要提示用户。
  3. 搜索结果存在多条数据时,务必注意分页数据获取,不要遗漏任何会议记录。

2. 整理会议纪要

  1. 整理纪要文档时默认给出纪要文档和逐字稿链接即可,无需读取纪要文档或逐字稿内容。
  2. 用户明确需要获取纪要文档中的总结、待办、章节产物时,再读取文档获取具体内容。

3. 纪要文档与逐字稿链接

  1. 纪要文档、逐字稿文档与关联的共享文档默认使用文档 Token 返回。
  2. 仅需要获取文档名称和 URL 等基本信息时,使用 lark-cli drive metas batch_query 查询
bash
# 学习命令使用方式
lark-cli schema drive.metas.batch_query

# 批量获取文档基本信息: 一次最多查询 10 个文档
lark-cli drive metas batch_query --data '{"request_docs": [{"doc_type": "docx", "doc_token": "<doc_token>"}], "with_url": true}'
  1. 需要获取文档内容时,使用 lark-cli docs +fetch。
bash
# 获取文档内容
lark-cli docs +fetch --doc <doc_token>

资源关系

Meeting (视频会议)
├── Note (会议纪要)
│   ├── MainDoc (主纪要文档)
│   ├── VerbatimDoc (逐字稿)
│   └── SharedDoc (会中共享文档)
└── Minutes (妙记)
    ├── Transcript (文字记录)
    ├── Summary (总结)
    ├── Todos (待办)
    └── Chapters (章节)

注意:+search 只能查询已结束的历史会议。查询未来的日程安排请使用 lark-calendar。

优先级:当用户搜索历史会议时,应优先使用 vc +search 而非 calendar events search。calendar 的搜索面向日程,vc 的搜索面向已结束的会议记录,支持按参会人、组织者、会议室等维度过滤。

路由规则:如果用户在问“开过的会”“今天开了哪些会”“最近参加过什么会”“已结束的会议”“历史会议记录”,优先使用 vc +search。只有在查询未来日程、待开的会、agenda 时才优先使用 lark-calendar。

特殊情况: 当用户查询“今天有哪些会议”时,通过 vc +search 查询今天开过的会议记录,同时使用 lark-calendar 技能查询今天还未开始的会议,统一整理后展示给用户。

Shortcuts(推荐优先使用)

Shortcut 是对常用操作的高级封装(lark-cli vc +<verb> [flags])。有 Shortcut 的操作优先使用。

Shortcut说明
+searchSearch meeting records (requires at least one filter)
+notesQuery meeting notes (via meeting-ids, minute-tokens, or calendar-event-ids)

API Resources

bash
lark-cli schema vc.<resource>.<method>   # 调用 API 前必须先查看参数结构
lark-cli vc <resource> <method> [flags] # 调用 API

重要:使用原生 API 时,必须先运行 schema 查看 --data / --params 参数结构,不要猜测字段格式。

meeting

  • get — 获取会议详情(主题、时间、参会人、note_id)
bash
# 获取会议基础信息:不包含参会人列表
lark-cli vc meeting get --params '{"meeting_id": "<meeting_id>"}'


# 获取会议基础信息:包含参会人列表
lark-cli vc meeting get --params '{"meeting_id": "<meeting_id>", "with_participants": true}'

minutes(跨域,详见 lark-minutes)

  • get — 获取妙记基础信息(标题、时长、封面);查询纪要内容请用 +notes --minute-tokens <minute-token>

权限表

方法所需 scope
+notes --meeting-idsvc:meeting.meetingevent:read、vc:note:read
+notes --minute-tokensvc:note:read、minutes:minutes:readonly、minutes:minutes.artifacts:read、minutes:minutes.transcript:export
+notes --calendar-event-idscalendar:calendar:read、calendar:calendar.event:read、vc:meeting.meetingevent:read、vc:note:read
+searchvc:meeting.search:read
meeting.getvc:meeting.meetingevent:read

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 Lark Vc AI skill do?

飞书视频会议:查询会议记录、获取会议纪要产物(总结、待办、章节、逐字稿)。1. 查询已经结束的会议数量或详情时使用本技能(如昨天 | 上周 | 今天已经开过的会议等场景),查询未开始的会议日程使用 lark-calendar 技能。2. 支持通过关键词、时间范围、组织者、参与者、会议室等筛选条件搜索会议记录。3. 获取或整理会议纪要时使用本技能。

Why use Lark Vc on TypingMind?

Because you install it once and use it with any model. Lark Vc 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 Vc 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-vc. 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 Lark Vc?

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 Vc?

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

Is the Lark Vc 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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