lark-workflow-meeting-summary
会议纪要整理工作流：汇总指定时间范围内的会议纪要并生成结构化报告。当用户需要整理会议纪要、生成会议周报、回顾一段时间内的会议内容时使用。
Best use case
lark-workflow-meeting-summary is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
会议纪要整理工作流：汇总指定时间范围内的会议纪要并生成结构化报告。当用户需要整理会议纪要、生成会议周报、回顾一段时间内的会议内容时使用。
Teams using lark-workflow-meeting-summary should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/lark-workflow-meeting-summary/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How lark-workflow-meeting-summary Compares
| Feature / Agent | lark-workflow-meeting-summary | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
会议纪要整理工作流：汇总指定时间范围内的会议纪要并生成结构化报告。当用户需要整理会议纪要、生成会议周报、回顾一段时间内的会议内容时使用。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
SKILL.md Source
# 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`](../lark-shared/SKILL.md)，其中包含认证、权限处理**。然后阅读 [`../lark-vc/SKILL.md`](../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：
```bash
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
```bash
# 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
1. 查询会议关联的纪要信息
```bash
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）
2. 获取纪要文档和逐字稿文档链接
```bash
# 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`](../lark-doc/SKILL.md) 学习云文档技能。
```bash
lark-cli docs +create --title "会议纪要汇总 (<start> - <end>)" --markdown "<内容>"
# 或追加到已有文档
lark-cli docs +update --doc "<url_or_token>" --mode append --markdown "<内容>"
```
## 参考
- [lark-shared](../lark-shared/SKILL.md) — 认证、权限（必读）
- [lark-vc](../lark-vc/SKILL.md) — `+search`、`+notes` 详细用法
- [lark-doc](../lark-doc/SKILL.md) — `+fetch`、`+create`、`+update` 详细用法Related Skills
req-change-workflow
Standardize requirement/feature changes in an existing codebase (especially Chrome extensions) by turning "改需求/需求变更/调整交互/改功能/重构流程" into a repeatable loop: clarify acceptance criteria, confirm current behavior from code, assess impact/risk, design the new logic, implement with small diffs, run a fixed regression checklist, and update docs/decision log. Use when the user feels the change process is chaotic, when edits tend to sprawl across files, or when changes touch manifest/service worker/OAuth/storage/UI and need reliable verification + rollback planning.
defou-workflow
将原始想法转化为结构清晰、判断明确、具有长期价值的“得否”风格内容报告。
defou-stanley-workflow
Defou x Stanley 融合工作流：结合深度结构化思考与人性弱点洞察，生成极简、犀利且具有长期价值的爆款内容。
standup-meeting
Conduct effective daily standup meetings for agile teams. Use when facilitating standups, tracking blockers, or improving team synchronization. Handles standup format, time management, and blocker resolution.
agentic-workflow
Practical AI agent workflows and productivity techniques. Provides optimized patterns for daily development tasks such as commands, shortcuts, Git integration, MCP usage, and session management.
daily-meeting-update
Interactive daily standup/meeting update generator. Use when user says 'daily', 'standup', 'scrum update', 'status update', 'what did I do yesterday', 'prepare for meeting', 'morning update', or 'team sync'. Pulls activity from GitHub, Jira, and Claude Code session history. Conducts 4-question interview (yesterday, today, blockers, discussion topics) and generates formatted Markdown update.
workflow-patterns
Use this skill when implementing tasks according to Conductor's TDD workflow, handling phase checkpoints, managing git commits for tasks, or understanding the verification protocol.
workflow-orchestration-patterns
Design durable workflows with Temporal for distributed systems. Covers workflow vs activity separation, saga patterns, state management, and determinism constraints. Use when building long-running processes, distributed transactions, or microservice orchestration.
workflow-automation
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
tdd-workflows-tdd-refactor
Use when working with tdd workflows tdd refactor
tdd-workflows-tdd-red
Generate failing tests for the TDD red phase to define expected behavior and edge cases.
tdd-workflows-tdd-green
Implement the minimal code needed to make failing tests pass in the TDD green phase.