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-workflow-meeting-summarygit 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-workflow-meeting-summary)<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.
<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>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 | $0.00055 | $0.01550 |
| Opus 5 | $0.00028 | $0.00775 |
| Sonnet 5 | $0.00011 | $0.00310 |
| Haiku 4.5 | $0.00006 | $0.00155 |
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.
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:
- 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
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为包含当天的日期(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。- 有
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
- 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。
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.
- 10d ago First seen · 117 lines · 55 tokens per session scan A 9743afd59e90
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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