lark-vc

larksuite/cli · updated Apr 8, 2026

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$npx skills add https://github.com/larksuite/cli --skill lark-vc
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summary

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

skill.md

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. 读取智能纪要(note_doc_token)内容时,纪要文档的第一个 <whiteboard> 标签是封面图(AI 生成的总结可视化),应同时下载展示给用户:
# 1. 读取纪要内容
lark-cli docs +fetch --doc <note_doc_token>
# 2. 从返回的 markdown 中提取第一个 <whiteboard token="xxx"/> 的 token
# 3. 下载封面图到 artifact 目录(和逐字稿同目录,保持产物归拢)
#    并非所有纪要都有封面画板,没有 <whiteboard> 标签时跳过即可
lark-cli docs +media-download --type whiteboard --token <whiteboard_token> --output ./artifact-<title>/cover

产物目录规范:同一会议的所有下载产物(封面图、逐字稿等)统一放到 artifact-<title>/ 目录下,不要散落在当前工作目录。

note_doc_token vs verbatim_doc_token — 两份不同的文档,根据用户意图选择:

  • note_doc_token智能纪要(AI 总结 + 待办 + 章节)— 用户说"纪要""总结""待办""纪要内容"时用这个
  • verbatim_doc_token逐字稿(完整的逐句文字记录,含说话人和时间戳)— 用户说"逐字稿""完整记录""谁说了什么"时用这个
  • 用户意图不明确时,应展示两个文档链接让用户选择,而不是替用户决定

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
# 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
# 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
+search Search meeting records (requires at least one filter)
+notes Query meeting notes (via meeting-ids, minute-tokens, or calendar-event-ids)

API Resources

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)
# 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-ids vc:meeting.meetingevent:readvc:note:read
+notes --minute-tokens vc:note:readminutes:minutes:readonlyminutes:minutes.artifacts:readminutes:minutes.transcript:export
+notes --calendar-event-ids calendar:calendar:readcalendar:calendar.event:readvc:meeting.meetingevent:readvc:note:read
+search vc:meeting.search:read
meeting.get vc:meeting.meetingevent:read
how to use lark-vc

How to use lark-vc on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add lark-vc
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/larksuite/cli --skill lark-vc

The skills CLI fetches lark-vc from GitHub repository larksuite/cli and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/lark-vc

Reload or restart Cursor to activate lark-vc. Access the skill through slash commands (e.g., /lark-vc) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.775 reviews
  • Naina Johnson· Dec 28, 2024

    lark-vc fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Alexander Haddad· Dec 28, 2024

    Keeps context tight: lark-vc is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Shikha Mishra· Dec 12, 2024

    Registry listing for lark-vc matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Camila Menon· Dec 4, 2024

    Solid pick for teams standardizing on skills: lark-vc is focused, and the summary matches what you get after install.

  • Nikhil Bhatia· Dec 4, 2024

    lark-vc has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Carlos Haddad· Nov 23, 2024

    Registry listing for lark-vc matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Carlos Khan· Nov 23, 2024

    Keeps context tight: lark-vc is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Arjun Farah· Nov 19, 2024

    We added lark-vc from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Mateo Shah· Nov 19, 2024

    lark-vc has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 3, 2024

    Solid pick for teams standardizing on skills: lark-vc is focused, and the summary matches what you get after install.

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