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Lark Workflow Chat Digest

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liangdabiao
lark-workflow-chat-digest

群消息日报生成器:拉取指定群聊在时间范围内的消息记录,AI 自动提取关键信息,生成结构化日报并可选保存为文档或发送到群聊。当用户需要'群日报'、'群消息摘要'、'生成日报'、'群聊总结'、'今日群消息回顾'、'帮我总结一下群里聊了什么'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-chat-digest
Stars
68
Forks
10
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by liangdabiao on GitHub. Read the source before you install it.

Installation

Install the Lark Workflow Chat Digest AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-chat-digest
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/liangdabiao/lark-workflow-feishu-cli.git /tmp/lark-workflow-feishu-cli
mkdir -p .claude/skills
cp -r /tmp/lark-workflow-feishu-cli/.claude/skills/lark-workflow-chat-digest .claude/skills/lark-workflow-chat-digest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Chat Digest in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Lark Workflow Chat Digest on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Lark Workflow Chat Digest is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

群消息日报生成器工作流

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

适用场景

  • "帮我总结一下今天项目群聊了什么" / "群日报"
  • "生成XX群的周报" / "群消息摘要"
  • "这个群今天有什么重要消息" / "群聊总结"
  • "帮我看看今天群里有没有重要通知" / "今日群消息回顾"
  • "生成本周产品群的讨论摘要"

前置条件

仅支持 user 身份。执行前确保已授权:

bash
lark-cli auth login --domain im
# 如需保存文档,还需:
lark-cli auth login --domain im,docs

工作流

{群名称/群ID + 时间范围}
  │
  ├── im +chat-search ──────────────► 查找群聊(获取 chat_id)
  │
  ├── im +chat-messages-list ───────► 拉取群消息(分页,max 50/页)
  │     └── --start / --end 过滤时间范围
  │
  ├── AI 分析消息 ──────────────────► 提取关键信息
  │     ├── 话题分类
  │     ├── 决策记录
  │     ├── 待办事项
  │     ├── 问题与风险
  │     └── 重要通知
  │
  ├── AI 生成结构化日报 ────────────► Markdown 格式
  │
  ├── [可选] docs +create ──────────► 保存为飞书文档
  │
  └── [可选] im +messages-send ─────► 发送到群聊

Step 1: 查找群聊

如果用户提供了群名称,先获取 chat_id:

bash
lark-cli im +chat-search --query "<群名称>" --as user

从返回结果中提取 chat_id(oc_xxx 格式)。如果用户直接提供了 chat_id,跳过此步。

Step 2: 拉取群消息

bash
# 拉取指定时间范围内的消息(按时间倒序,最新在前)
lark-cli im +chat-messages-list \
  --chat-id "<chat_id>" \
  --start "2026-04-09T00:00:00+08:00" \
  --end "2026-04-09T23:59:59+08:00" \
  --page-size "50" \
  --sort "asc" \
  --as user

分页处理

每页最多 50 条消息。如果返回结果中有 has_more: true,用 --page-token 继续拉取:

bash
# 第一页
lark-cli im +chat-messages-list --chat-id "oc_xxx" --start "..." --end "..." --page-size "50" --as user
# 返回 page_token 后,拉取下一页
lark-cli im +chat-messages-list --chat-id "oc_xxx" --start "..." --end "..." --page-size "50" --page-token "<token>" --as user

注意:

  • 如果一天的消息量超过 500 条,建议只分析最近 200-300 条以避免超出上下文
  • 过滤掉纯表情回复、系统消息、图片/文件消息(除非用户特别要求)
  • 用 --sort asc 按时间正序排列,便于 AI 理解讨论脉络

Step 3: AI 生成日报

将拉取到的消息交给 AI 分析,按以下结构生成日报:

markdown
## {群名} 日报 — {日期}

### 概览
- 消息总数:N 条
- 活跃成员:N 人
- 主要话题:{2-3个关键词}

### 重要决策
| 决策内容 | 提出者 | 时间 |
|---------|--------|------|

### 待办事项
| 事项 | 负责人 | 来源 |
|------|--------|------|

### 话题讨论
#### 1. {话题标题}
- **摘要**:一句话概括
- **参与人**:A、B、C
- **结论**:{如有}

#### 2. {话题标题}
...

### 问题与风险
| 问题描述 | 提出者 | 状态 |
|---------|--------|------|

### 其他通知
- {通知1}
- {通知2}

分析规则:

  • 忽略无意义的闲聊和纯表情消息
  • 重点关注包含决策、待办、问题的消息
  • 保留关键上下文,但去除冗余讨论
  • 如果消息中包含 @某人 的提醒,在待办中标注

Step 4: 输出日报(可选)

发送到群聊

bash
lark-cli im +messages-send \
  --chat-id "<chat_id>" \
  --markdown "{日报内容}" \
  --as bot

发送给个人

bash
lark-cli im +messages-send \
  --user-id "<open_id>" \
  --markdown "{日报内容}" \
  --as bot

保存为文档

bash
lark-cli docs +create \
  --title "{群名} 日报 — {日期}" \
  --markdown "{日报内容}" \
  --as user

降级策略

场景降级方案
找不到群聊提示用户确认群名称,或直接使用 chat_id
群内无消息返回"该时段内群内无消息"
消息量过大只分析最近 N 条,其余折叠为"N 条消息未分析"
im 推送失败仅输出到对话,告知用户手动发送
docs 保存失败仅输出 Markdown 到对话
消息内容为图片/文件在日报中标注"[图片/文件]",建议用户手动查看

权限表

命令所需 scope
im +chat-searchim:chat:readonly
im +chat-messages-listim:message:readonly
im +messages-sendim:message
docs +createdocx:document:create

参考

Frequently asked questions

What does the Lark Workflow Chat Digest AI skill do?

群消息日报生成器:拉取指定群聊在时间范围内的消息记录,AI 自动提取关键信息,生成结构化日报并可选保存为文档或发送到群聊。当用户需要'群日报'、'群消息摘要'、'生成日报'、'群聊总结'、'今日群消息回顾'、'帮我总结一下群里聊了什么'时使用。

Why use Lark Workflow Chat Digest on TypingMind?

Because you install it once and use it with any model. Lark Workflow Chat Digest is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Lark Workflow Chat Digest in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/liangdabiao/lark-workflow-feishu-cli/tree/main/.claude/skills/lark-workflow-chat-digest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Chat Digest?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Lark Workflow Chat Digest?

As many as you like. As long as a model supports skills, you can use Lark Workflow Chat Digest with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Lark Workflow Chat Digest AI skill free?

It is published on GitHub by liangdabiao. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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