Feishu Doc
openclaw/openclaw
Feishu document read/write workflows. An agent skill from openclaw/openclaw.
飞书视频会议：搜索历史会议记录、查询会议纪要（总结/待办/章节/逐字稿）、查询参会人快照。当用户查询已结束的会议、获取会议产物（纪要/妙记）、查看参会人时使用；查询未来日程走 lark-calendar。不负责：Agent 真实入会/离会、会中实时事件（走 lark-vc-agent）。
Install Lark Vc
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install rongxinzy/RongxinAI lark-vc --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .claude/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .claude/skills/lark-vc && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .claude/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vcType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install rongxinzy/RongxinAI lark-vc --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .agents/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .agents/skills/lark-vc && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .agents/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install rongxinzy/RongxinAI lark-vc --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .cursor/skills/lark-vc && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .cursor/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/rongxinzy/RongxinAI.git --path MCPs/feishu/skills/lark-vc--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install rongxinzy/RongxinAI lark-vc --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .gemini/skills/lark-vc && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .gemini/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install rongxinzy/RongxinAI lark-vcInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .github/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .github/skills/lark-vc && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .github/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add rongxinzy/RongxinAI --skill lark-vc -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install rongxinzy/RongxinAI lark-vc --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/rongxinzy/RongxinAI.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/MCPs/feishu/skills/lark-vc .opencode/skills/lark-vc && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "lark-vc" agent skill from https://github.com/rongxinzy/RongxinAI/tree/main/MCPs/feishu/skills/lark-vc into .opencode/skills/lark-vc/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "lark-vc", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
lark-vc- GitHub stars
- 154
- Used in
- 2 other repos
- Token cost
- ~2.4k tokens
- SKILL.md length
- 463 words
- Files
- 5 (incl. references)
- Skills in repo
- 94
- Repo updated
- First seen
- Licence
- AGPL-3.0
At a glance
飞书视频会议：搜索历史会议记录、查询会议纪要（总结/待办/章节/逐字稿）、查询参会人快照。当用户查询已结束的会议、获取会议产物（纪要/妙记）、查看参会人时使用；查询未来日程走 lark-calendar。不负责：Agent 真实入会/离会、会中实时事件（走 lark-vc-agent）。
- Works in 4 steps: 搜索会议记录 → 整理会议纪要 → 纪要文档与逐字稿链接 → …
- Tasks that involve Messaging and chat bots
- SKILL.md covers 身份, Shortcuts （推荐优先使用）, 意图路由 and 核心概念, plus 5 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
What it does
Lark Vc is an agent skill from rongxinzy/RongxinAI. 飞书视频会议：搜索历史会议记录、查询会议纪要（总结/待办/章节/逐字稿）、查询参会人快照。当用户查询已结束的会议、获取会议产物（纪要/妙记）、查看参会人时使用；查询未来日程走 lark-calendar。不负责：Agent 真实入会/离会、会中实时事件（走 lark-vc-agent）。
Its SKILL.md is about 2.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 5 other files, including reference files (for example `references/lark-vc-detail.md`, `references/lark-vc-recording.md` and `references/lark-vc-search.md`).
It sits in Productivity & Automation, covering Messaging and chat bots. The repository describes itself as: An all-in-one local AI Agent workspace with a fully self-developed stack. The licence is AGPL-3.0.
When your agent uses it
- Tasks that involve Messaging and chat bots
Example prompts
- “/lark-vc”
Workflow steps
4 steps, taken from the step headings in SKILL.md.
What it can do on your machine
Read from SKILL.md and the folder at commit 9c64865. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Lark Vc loads about 2.4k tokens when it runs, and up to ~9.4k if it reads all its reference files. Until then it costs about 38 tokens; SKILL.md has 463 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from rongxinzy/RongxinAI at commit 9c64865, republished under its AGPL-3.0 licence (© rongxinzy). 463 words, ~2,376 tokens.
Download SKILL.mdSave it as
.claude/skills/lark-vc/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.- name
- lark-vc
- description
- 飞书视频会议：搜索历史会议记录、查询会议纪要（总结/待办/章节/逐字稿）、查询参会人快照。当用户查询已结束的会议、获取会议产物（纪要/妙记）、查看参会人时使用；查询未来日程走 lark-calendar。不负责：Agent 真实入会/离会、会中实时事件（走 lark-vc-agent）。
- version
- 1.0.0
- metadata.cliHelp
- lark-cli vc --help
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
CRITICAL — 开始前 MUST 先用 Read 工具读取 references/vc-domain-boundaries.md，不读将导致命令使用、会议产物决策、领域边界职责判断错误：
- 了解日历 & VC、会议产物 & 文档的关联关系和职责划分
- 了解会议产物（妙记和纪要）之间的关联关系，例如：妙记和纪要产生条件相互独立
- 了解不同会议产物的组成部分，以便根据需求决策使用哪种产物的数据
- 了解会议总结、分析和信息提取的标准流程
身份
所有 vc 命令默认使用 --as user。+search 和 meeting get 也支持 --as bot。
# BAD — 查昨天的会议用 calendar，会漏掉即时会议
lark-cli calendar +search-event --query "站会" --start <start_time> --end <end_time>
# GOOD — 查已结束的会议用 vc +search
lark-cli vc +search --query "站会" --start <start_time> --end <end_time>Shortcuts （推荐优先使用）
| Shortcut | 说明 |
|---|---|
+search | 搜索历史会议记录（需至关键词、时间范围、组织者、参与者、会议室少一个筛选条件） |
+detail | 通过 meeting-ids 获取会议详情，包括 note_id 和 minute_token |
+recording | 通过 meeting-ids 或 calendar-event-ids 查询 minute_token |
- 使用任何 Shortcut 前，必须先读其对应 reference 文档。
意图路由
| 用户意图 | 路由到 |
|---|---|
| 查"昨天的会议""上周的会""已结束的会议" | 本 skill（+search，含即时会议） |
| 查日历/日程或未来时间的会议 | lark-calendar |
| 查"今天有哪些会议" | vc +search（已结束）+ lark-calendar（未开始），合并展示 |
| 只按自然语言标题查"xx 纪要的逐字稿 / 原始记录 / 谁说了什么" | 先到 lark-drive / lark-doc；仅在已拿到 note_id / vc-node-id 后再到 lark-note |
| Agent 真实入会/离会、会中实时事件 | lark-vc-agent |
| 妙记信息/时长/封面/链接 | 先走 vc +detail 或 vc +recording 获取 minute_token，再用 lark-minutes 的 minutes get |
| 本地音视频文件转纪要/逐字稿 | 先走 lark-minutes 上传，再用 minutes +detail --minute-tokens |
核心概念
- 视频会议（Meeting）：飞书视频会议实例，通过 meeting_id 标识。已结束的会议支持通过关键词、时间段、参会人、组织者、会议室等条件搜索（见
+search）。 - 会议纪要（Note）：视频会议结束后生成的结构化文档，通过
note_id标识，包含纪要文档（总结、待办）和逐字稿文档。note_display_type区分**普通纪要（normal）**和 unified 纪要；已知note_id的直查与 unified 原始记录请用 lark-note。 - 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，包含总结、待办、章节和文字记录，通过 minute_token 标识。妙记带有原始会议录制视频，会后不会自动授权给参会人，需管理员授权或参会人主动申请；而智能纪要及其逐字稿会后自动授权给参会人。
- 纪要文档（MainDoc）：AI 智能纪要的主文档，包含 AI 生成的总结和待办，对应
note_doc_token。 - 用户会议纪要（MeetingNotes）：用户主动绑定到日程的纪要文档，对应
meeting_note。需先通过calendar +meeting由event_id获取。 - 逐字稿（VerbatimDoc）：会议的逐句文字记录，包含说话人和时间戳。
产物选择决策
| 用户意图 | 必须读取的产物 | 禁止 |
|---|---|---|
| 提炼/总结/重新总结/整理会议内容/回顾会议 | 为降低 token 消耗，非必须不得获取 AI 纪要。必须使用原始对话记录（按下方逐字稿路由取得），基于原始对话独立分析。两类产物都存在且用户未指定时，默认用智能纪要的逐字稿；用户明确要妙记时才用妙记文字记录（Transcript） | 禁止直接搬运 AI 纪要（note_doc_token）的总结作为最终输出 |
| 查看待办/章节 | 默认 AI 纪要（note_doc_token）；仅存在妙记或用户明确要妙记时用妙记产物 — AI 待办更友好（含提出人和负责人），章节按话题划分更结构化 | — |
| 查看纪要链接/文档地址 | 仅返回文档链接，无需读取内容 | — |
| 直接看 AI 总结结果 | AI 纪要（note_doc_token） | — |
| 谁说了什么/完整发言记录 | 原始对话记录（按下方逐字稿路由取得） | — |
智能纪要 vs 妙记的选择规则（总结/待办/逐字稿等重复产物通用）：只存在一类 → 用存在的那类；两类都存在且用户明确指定（如"看妙记逐字稿"）→ 语义指向哪个走哪个，不要改道；两类都存在但用户未指定 → 默认智能纪要及其逐字稿（会后自动授权给参会人，访问门槛低于含原始录制视频、需申请授权的妙记）。完整说明见
references/vc-domain-boundaries.md的「产物选择决策」。
逐字稿路由：先用
vc +detail拿到note_id，再note +detail看note_display_type，不要只看verbatim_doc_token是否为空。具体路由以 lark-note 的note_display_type规则为准。为什么"提炼/总结"必须从原始对话记录出发？ AI 纪要是模型对会议的二次压缩，可能遗漏讨论细节、争论过程和隐含决策。用户要求"提炼"或"重新总结"时，期望的是基于原始对话的独立分析，而非对 AI 产物的重新排版。
核心场景
1. 搜索会议记录
- 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。
- 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。
- 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。
- 只有自然语言纪要标题、没有会议线索时，不要把标题当会议关键词；按上方意图路由切到文档搜索。
2. 整理会议纪要
在选择读取哪个产物前，先确认你理解 AI 总结链路 vs 录制链路的区别。如不确定，先读
references/vc-domain-boundaries.md。
- 整理纪要文档时默认给出纪要文档、逐字稿、妙记链接即可，无需读取纪要文档或逐字稿内容。
- 用户明确需要获取总结、待办、章节产物时，再读取文档获取具体内容。
- 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> --doc-format markdown
# 2. 从返回的 markdown 中提取第一个 <whiteboard token="xxx"/> 的 token
# 3. 下载封面图到聚合目录（和逐字稿、录像同目录，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 ./minutes/<minute_token>/cover产物目录规范：同一会议的所有下载产物（录像、逐字稿、封面图等）统一放到
./minutes/{minute_token}/目录下。这与minutes +download和minutes +detail --minute-tokens的默认落点保持一致，便于 Agent 聚合。显式路径（如封面图）需手动对齐到同一目录。
纪要相关文档 — 根据用户意图选择：
note_doc_token→ AI 智能纪要（AI 总结 + 待办），由note +detail --note-id <note_id>返回meeting_note→ 用户绑定到日程的会议纪要，由calendar +meeting --event-ids <event_id>返回- 用户说"逐字稿""完整记录""谁说了什么"时 → 按
note_display_type路由，详见 lark-note- 用户说"纪要""总结""纪要内容"时，应同时返回
note_doc_token和meeting_note（如有）- 用户意图不明确时，应展示所有文档链接让用户选择，而不是替用户决定
- 如果用户提供的是本地音视频文件并说"转纪要""转逐字稿"，不要直接从
vc +detail开始；应先用 minutes +upload 生成minute_url，再提取minute_token调用minutes +detail --minute-tokens
Show full SKILL.md (182 more words)Show less
3. 纪要文档与逐字稿链接
- 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。
- 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}'- 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> --doc-format markdown4. 查询参会人快照（读操作）
用户问"谁参加过这场会议""这个会议有哪些参会人""某某参会了吗"等参会人快照类问题时，使用 vc meeting get --with-participants：这是参会人服务端快照 API，不依赖 bot 身份参会，已结束会议也可查：
lark-cli vc meeting get --params '{"meeting_id":"<meeting_id>","with_participants":true}'选型判断表：
| 用户意图 | 推荐命令 | 所在 skill |
|---|---|---|
| 参会人快照（谁参加过、何时入/离会，任意时点） | vc meeting get --with-participants | 本 skill |
| 已结束会议的发言内容 | 优先：vc +detail 取 note_id 再 note +detail 取 verbatim_doc_token 后 docs +fetch；备选：vc +detail 取 minute_token 再 minutes +detail --transcript | lark-note / lark-minutes |
| 进行中会议的实时事件流（转写、聊天、共享、会中加入/离开） | vc +meeting-events | lark-vc-agent |
| Agent 真实入会 / 离会 | vc +meeting-join / vc +meeting-leave | lark-vc-agent |
资源关系
Meeting (视频会议)
├── Note (会议纪要) ← note_id 标识，note_display_type: normal / unified
│ ├── MainDoc (AI 智能纪要文档, note_doc_token)
│ ├── MeetingNotes (用户绑定的会议纪要文档, meeting_notes)
│ ├── VerbatimDoc (逐字稿, verbatim_doc_token) ← normal 路径
│ ├── UnifiedTranscript (unified 原始记录) ← unified 路径，note +transcript（lark-note）
│ └── SharedDoc (会中共享文档)
└── Minutes (妙记) ← minute_token 标识，由 `vc +detail` 或 `vc +recording` 桥接获取，产物详情走 [lark-minutes](../lark-minutes/SKILL.md)
├── Transcript (文字记录)
├── Summary (总结)
├── Todos (待办)
├── Chapters (章节)
└── Keywords (推荐关键词)MeetingNotes 边界：用户绑定到日程的会议纪要文档（
meeting_note）属于日程域，不在 VC 资源关系内；从event_id用calendar +meeting获取。妙记边界：
+recording仅负责把meeting_id/calendar_event_id桥接到minute_token；妙记的总结/待办/章节/逐字稿等产物归 lark-minutes（minutes +detail）。Note 域边界：VC 域只负责把
meeting_id转成note_id/minute_token，纪要详情归 lark-note。
- 入口选择：从
meeting_id出发用vc +detail拿note_id和minute_token；从minute_token出发用minutes +detail也会返回关联的note_id，可继续走note +detail拿纪要文档 token。- 已有
note_id→ 直接走note +detail/note +transcript，不要绕回 VC。- 已有
doc_token且目标是读正文 → lark-doc。- 只有自然语言纪要标题 → 文档搜索 / Docx 正文读取；有显式
vc-node-id才进入 lark-note。- 从日程出发（只有
event_id）→ 先走calendar +meeting拿到meeting_id或meeting_note，再按上述路径继续。
API Resources
lark-cli vc <resource> <method> [flags]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（不含参会人）
lark-cli vc meeting get --params '{"meeting_id": "<meeting_id>"}'
# 获取会议基础信息（含参会人）
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）；查询妙记内容（总结/待办/章节/逐字稿）请用minutes +detail
不在本 skill 范围
- 查询未来的会议日程 → lark-calendar
- Agent 真实入会/离会、会中实时事件 → lark-vc-agent
- 只有纪要文档标题的逐字稿查询 → 文档搜索 / Docx 正文读取；有显式
vc-node-id才进入 lark-note - 本地音视频文件转纪要/逐字稿、妙记搜索/下载/上传/重命名/替换说话人 → lark-minutes
- 通过
note_id取纪要文档 Token → lark-note
© rongxinzy, AGPL-3.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 4 other files (references) in MCPs/feishu/skills/lark-vc of rongxinzy/RongxinAI.
- SKILL.md
- references/lark-vc-detail.md
- references/lark-vc-recording.md
- references/lark-vc-search.md
- references/vc-domain-boundaries.md
Open the folder on GitHubat commit 9c64865
Used in 2 other repositories
We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in rongxinzy/RongxinAI, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Lark Vc next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Lark Vc this skillrongxinzy/RongxinAI | 154 | 2 repos | ~2.4k | Automated safety check: Pass | AGPL-3.0 | |
| Feishu Docopenclaw/openclaw | 392k | — | ~516 | Automated safety check: Pass | MIT | |
| She Love Me863401402/she-love-me | 931 | 1 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Feishu Docraucvr/Group-Goki | 112 | 3 repos | ~592 | Automated safety check: Pass | MIT | |
| Wechat Article Extractorfreestylefly/wechat-article-extractor-skill | 136 | 1 repos | ~1k | Automated safety check: Pass | None | |
| Wechat Miniprogram Builderchenjin-cmd/wechat-miniprogram-builder | 356 | — | ~634 | Automated safety check: Pass | MIT |
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Questions about Lark Vc
What does Lark Vc do?
飞书视频会议：搜索历史会议记录、查询会议纪要（总结/待办/章节/逐字稿）、查询参会人快照。当用户查询已结束的会议、获取会议产物（纪要/妙记）、查看参会人时使用；查询未来日程走 lark-calendar。不负责：Agent 真实入会/离会、会中实时事件（走 lark-vc-agent）。. Lark Vc is an agent skill from rongxinzy/RongxinAI.
When should I use Lark Vc?
Lark Vc fits situations like: tasks that involve Messaging and chat bots.
How do I install Lark Vc in Claude Code?
Run `npx skills add rongxinzy/RongxinAI --skill lark-vc -a claude-code`. Or copy the skill folder (MCPs/feishu/skills/lark-vc in rongxinzy/RongxinAI) into .claude/skills/lark-vc in your project. Claude Code loads it when a task matches its description.
How do I install Lark Vc in Codex?
Run `npx skills add rongxinzy/RongxinAI --skill lark-vc -a codex`. Or copy the skill folder (MCPs/feishu/skills/lark-vc in rongxinzy/RongxinAI) into .agents/skills/lark-vc in your project. Codex loads it when a task matches its description.
Can I use Lark Vc in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add rongxinzy/RongxinAI --skill lark-vc -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/lark-vc, .gemini/skills/lark-vc, .github/skills/lark-vc and .opencode/skills/lark-vc in your project.
What does Lark Vc need to run?
SKILL.md names no scripts, command-line tools or credentials: Lark Vc is instructions for the agent only.
Does Lark Vc access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Lark Vc safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Lark Vc use?
Lark Vc is published under the AGPL-3.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Lark Vc use?
About 2.4k tokens (SKILL.md is roughly 9.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 7k tokens, read only when the agent opens those files.
What are the alternatives to Lark Vc?
Skills that share tags, products or a category with Lark Vc: Feishu Doc (openclaw/openclaw, 392k stars), She Love Me (863401402/she-love-me, 931 stars), Feishu Doc (raucvr/Group-Goki, 112 stars) and Wechat Article Extractor (freestylefly/wechat-article-extractor-skill, 136 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Lark Vc?
rongxinzy (a GitHub organization) maintains it in rongxinzy/RongxinAI, which has 154 GitHub stars. The repository holds 94 skills in this directory. The repository was last updated on October 10, 2026.
Source: rongxinzy/RongxinAI on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.