Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ddpie/lark-mcp-on-agentcore --skill lark-vcgit clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcoreWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-vc)<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-vc"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-vc/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-vc"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-vc.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.03788 |
| Opus 5 | $0.00048 | $0.01894 |
| Sonnet 5 | $0.00019 | $0.00758 |
| Haiku 4.5 | $0.00010 | $0.00379 |
Grade A, and why
lark-vc scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
vc (v1)
CRITICAL — 开始前 MUST 先调用 lark_get_skill(domain="vc", section="vc-domain-boundaries")，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
Shortcuts （推荐优先使用）
| Shortcut | 说明 |
|---|---|
lark_vc_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） |
lark_vc_detail |
通过 meeting_ids 获取会议详情，包括 note_id 和 minute_token |
lark_vc_recording |
通过 meeting_ids 或 calendar_event_ids 查询 minute_token |
- 使用任何 Shortcut 前，必须先调用对应的
lark_get_skill(domain="vc", section="...")了解参数和返回值结构。
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（lark_vc_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" | lark_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 lark_vc_detail 或 lark_vc_recording 获取 minute_token，再用 lark-minutes 的 lark_invoke(tool_name="lark_minutes_minutes_get", ...) |
| 本地音视频文件转纪要/逐字稿 | 先走 lark-minutes 上传，再用 lark_minutes_detail(minute_tokens="<minute_token>") |
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
lark_vc_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。
- 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 lark-calendar 的lark_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）或妙记文字记录（Transcript），基于原始对话独立分析 | 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 | AI 纪要（note_doc_token）或妙记产物 — 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） | — |
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 192 lines · 95 tokens per session scan A 009330c00de7
lark-vc is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 12d ago), licensed MIT. It adds 95 tokens to every session and 3,788 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
lark-cli
A command-line tool for working with Lark, also called Feishu, a workplace collaboration platform. It covers services such as calendars, meetings, documents, spreadsheets, messaging, tasks, approvals, and shared files.
minutes
Access Lark Minutes recordings - get metadata, export transcripts, download audio/video. Use when user asks about meeting recordings, transcripts, or minutes.
larksnap-fetch
A bridge for downloading Feishu/Lark documents or ordinary webpages into local files, with options such as Markdown, HTML, or PDF. Feishu, also called Lark, is a workplace collaboration platform.
notion
Use the Notion API to create/read/update pages, data sources (databases), and blocks.
google-workspace
Use the native Google Workspace tools for Gmail, Calendar, Drive, Docs, and Sheets.
claude-to-im
Bridge THIS Claude Code or Codex session to Telegram, Discord, Feishu/Lark, QQ, or WeChat so the user can chat with Claude from their phone. Use for: setting up, starting, stopping, or diagnosing the claude-to-im bridge daemon; forwarding Claude replies to a messaging app; any phrase like "claude-to-im", "bridge"…