lark-workflow-meeting-summary

lark-workflow-meeting-summary is a skill for Claude Code, Codex from ddpie/lark-mcp-on-agentcore. It costs 55 tokens per session (1,550 once invoked), scanned A, original, MIT.

A workflow for collecting meeting notes over a chosen period and turning them into a structured report. Feishu is a workplace collaboration service.

In plain words
What is it for?
Reviewing meetings from today, this week, or another period, then producing a meeting summary or weekly report.
Why use it?
It combines meeting searches, meeting details, notes, and document information into one repeatable review process.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Reviewing meetings from today, this week, or another period, then producing a meeting summary or weekly report.

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Install with agentmods
npx agentmods add skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary
Install

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.

Any agent
npx skills add ddpie/lark-mcp-on-agentcore --skill lark-workflow-meeting-summary
Clone the repo
git clone --depth 1 https://github.com/ddpie/lark-mcp-on-agentcore

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for lark-workflow-meeting-summary

README.md
[![agentmods](https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary/github.svg)](https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary)
Your own site
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary/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.

agentmods 80×15 button for lark-workflow-meeting-summary

Your own site · 80×15
<a href="https://agentmods.dev/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary"><img src="https://agentmods.dev/badge/skills/ddpie/lark-mcp-on-agentcore/lark-workflow-meeting-summary.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,550 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00055 $0.01550
Opus 5 $0.00028 $0.00775
Sonnet 5 $0.00011 $0.00310
Haiku 4.5 $0.00006 $0.00155

Measured 10d ago against content hash 9743afd59e90, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

lark-workflow-meeting-summary 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 10d 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.

docker/skills/lark-workflow-meeting-summary/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

会议纪要汇总工作流

(authentication is handled automatically by the MCP server)

调用前先调用 lark_get_skill(domain="vc") 了解会议纪要相关操作。

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:

  1. 了解日历 & VC、会议产物 & 文档的关联关系和职责划分
  2. 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
  3. 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
  4. 了解会议总结、分析和信息提取的标准流程

适用场景

  • "帮我整理这周的会议纪要" / "总结最近的会议" / "生成会议周报"
  • "看看今天开了哪些会" / "回顾过去一周开了哪些会"

前置条件

仅支持 user 身份

工作流

{时间范围} ─► lark_vc_search ──► 会议列表 (meeting_ids)
                   │
                   ▼
               lark_vc_detail ──► 获取 note_id
                   │
                   ▼
               lark_note_detail ──► 纪要文档 tokens
                   │
                   ▼
               lark_invoke(tool_name="lark_drive_metas_batch_query") 纪要元数据
                   │
                   ▼
               结构化报告

Step 1: 确定时间范围

默认过去 7 天。推断规则:"今天"→当天,"这周"→本周一now,"上周"→上周一上周日,"这个月"→1日~now。

注意:日期转换必须调用系统命令(如 date),不要心算。时间范围参数需根据工具实际要求格式化(通常为 YYYY-MM-DD 或 ISO 8601)。

Step 2: 查询会议记录

lark_vc_search(start="<YYYY-MM-DD>", end="<YYYY-MM-DD>", format="json", page_size="30")
  • 时间范围拆分:搜索的时间范围最大为 1 个月。搜索更长时间范围的会议,需要拆分为多次时间范围为一个月查询。
  • end包含当天的日期(即查"今天"时 start 和 end 都填今天)
  • format="json" 输出 JSON 格式,你更佳擅长解析 JSON 数据。
  • page_size="30" 每页最多 30 条。
  • page_token 时必须继续翻页,收集所有 id 字段(meeting-id)

Step 3: 获取纪要元数据

  1. 查询会议关联的纪要信息
# 首先获取 note_id 和 minute_token
lark_vc_detail(meeting_ids="id1,id2,...,idN")

# 然后用 note_id 获取文档 tokens(如有多个需分别获取)
lark_note_detail(note_id="note_id")
  • 根据上一步搜集到的 meeting-id 查询。
  • 单次最多查询 50 个,超过 50 个需分批调用。
  • 部分会议没有 note_id 或报错 no notes available,在最终输出中标注"无纪要"。
  • 记录每个纪要的 note_id(纪要 ID)、note_display_type(展示类型:unknown / normal / unified)、note_doc_token(纪要文档 Token)和 verbatim_doc_token(逐字稿文档 Token)。

逐字稿路由按 note_display_type 决定(详见 lark_get_skill(domain="vc", section="vc-domain-boundaries") 的 Note 域):

  • normal:逐字稿是独立文档,链接/正文走 verbatim_doc_token
  • unified:逐字稿不是独立文档,没有可分享的逐字稿文档链接;需要逐字稿内容时用 lark_note_transcript(note_id="<note_id>")(参见 lark_get_skill(domain="note"))拉取到本地,报告中标注"unified 纪要"即可。

Read the full file on GitHub · 117 lines

Changes

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.

  1. 10d ago First seen · 117 lines · 55 tokens per session scan A 9743afd59e90

Subscribe to this mod's changes

lark-workflow-meeting-summary is a skill published in the GitHub repository ddpie/lark-mcp-on-agentcore (8 stars, last pushed 13d ago), licensed MIT. It adds 55 tokens to every session and 1,550 once invoked, about $0.0003 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.

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