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 appleweiping/WEIPING_WIKI --skill lark-workflow-meeting-summarygit clone --depth 1 https://github.com/appleweiping/WEIPING_WIKIWrote 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/appleweiping/weiping_wiki/lark-workflow-meeting-summary)<a href="https://agentmods.dev/skills/appleweiping/weiping_wiki/lark-workflow-meeting-summary"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/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/appleweiping/weiping_wiki/lark-workflow-meeting-summary"><img src="https://agentmods.dev/badge/skills/appleweiping/weiping_wiki/lark-workflow-meeting-summary.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 | $0.00055 | $0.01218 |
| Opus 5 | $0.00028 | $0.00609 |
| Sonnet 5 | $0.00011 | $0.00244 |
| Haiku 4.5 | $0.00006 | $0.00122 |
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 8d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- lark-workflow-meeting-summary — 100% identical, 0 lines differ
- lark-workflow-meeting-summary — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 104 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
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。然后阅读 ../lark-vc/SKILL.md,了解会议纪要相关操作。
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。执行前确保已授权:
lark-cli auth login --domain vc # 基础(查询+纪要)
lark-cli auth login --domain vc,drive # 含读取纪要文档正文、生成文档
工作流
{时间范围} ─► vc +search ──► 会议列表 (meeting_ids)
│
▼
vc +notes ──► 纪要文档 tokens
│
▼
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 CLI 实际要求格式化(通常为YYYY-MM-DD或 ISO 8601)。
Step 2: 查询会议记录
# page-size 最大为 30
lark-cli 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
lark-cli vc +notes --meeting-ids "id1,id2,...,idN"
- 根据上一步搜集到的
meeting-id查询会议纪要。 - 单次最多查询 50 个纪要信息,超过 50 个需分批调用。
- 部分会议返回
no notes available,在最终输出中标注"无纪要" - 记录每个会议的
note_doc_token(纪要文档 Token)和verbatim_doc_token(逐字稿文档 Token)
- 获取纪要文档和逐字稿文档链接
# 学习命令使用方式
lark-cli schema drive.metas.batch_query
# 批量获取纪要文档与逐字稿链接: 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}'
Step 4: 整理纪要报告
根据时间跨度选择输出格式:
- 单日汇总("今天"/"昨天"):用"今日会议概览"标题,逐会议列出会议时间、主题、纪要链接、逐字稿链接。
- 多日/周报("这周"/"过去 7 天"等):用"会议纪要周报"标题,含概览统计、逐会议详情。
Step 5: 生成文档(可选,用户要求时)
阅读 ../lark-doc/SKILL.md 学习云文档技能。
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.
- 8d ago First seen · 104 lines · 55 tokens per session scan A 97b34c388538
lark-workflow-meeting-summary is a skill published in the GitHub repository appleweiping/WEIPING_WIKI (122 stars, last pushed 16d ago), licensed MIT. It adds 55 tokens to every session and 1,218 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 2026-09-03.
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