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Lark Workflow Task Digest

Community
liangdabiao
lark-workflow-task-digest

任务状态播报员:查询任务列表并按状态分类统计,生成进度报告,通过飞书消息推送或保存为文档。当用户需要'任务进度'、'任务报告'、'任务状态'、'待办汇报'、'项目进度报告'、'任务概览'、'今日任务完成情况'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-task-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 Task 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-task-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-task-digest .claude/skills/lark-workflow-task-digest
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Task 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 Task 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 Task 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.

任务状态播报员工作流

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

  • "今天任务完成情况怎么样" / "任务进度"
  • "生成本周任务周报" / "任务报告"
  • "看看我有哪些待办" / "待办汇报"
  • "项目进度报告" / "任务概览"
  • "帮我播报一下任务状态"

前置条件

仅支持 user 身份。执行前确保已授权:

bash
lark-cli auth login --domain task
# 如需推送消息或保存文档,还需:
lark-cli auth login --domain task,im,docs

工作流

{时间范围 / 状态筛选}
  │
  ├── task +get-my-tasks [--page-all] ──► 任务列表
  │
  ├── AI 分类统计 ─────────────────────► 按状态/优先级/截止日分组
  │     ├── 未开始
  │     ├── 进行中
  │     ├── 已完成
  │     └── 已逾期(高亮)
  │
  ├── AI 生成进度报告 ─────────────────► 结构化 Markdown
  │
  ├── [可选] im +messages-send ────────► 推送到飞书
  │
  └── [可选] docs +create ─────────────► 保存为文档

Step 1: 确定时间范围

根据用户请求确定查询范围:

用户说范围
今天 / 今日当天
本周本周一 ~ 今天
本月本月 1 号 ~ 今天

注意:日期计算使用系统命令 date,不要心算。

bash
# 获取今天的截止范围(示例)
date -d "today 23:59:59" +%s   # Unix timestamp

Step 2: 获取任务列表

bash
# 默认:返回分配给当前用户的未完成任务(最多 20 条)
lark-cli task +get-my-tasks

# 获取全部未完成任务(超过 20 条时)
lark-cli task +get-my-tasks --page-all

# 按截止日期筛选(推荐,减少数据量)
lark-cli task +get-my-tasks --due-end "2026-04-09T23:59:59+08:00"

# 按创建时间筛选
lark-cli task +get-my-tasks --created-at "2026-04-01"

# 包含已完成的任务
lark-cli task +get-my-tasks --complete

# 搜索特定任务
lark-cli task +get-my-tasks --query "<关键词>"

注意:不带过滤条件时可能返回大量历史待办,建议用 --due-end 过滤。如果数据量仍过大(超过上下文限制),AI 汇总时只展示近 30 天内创建的,其余折叠为"其他 N 项历史待办"。

Step 3: AI 生成进度报告

将 Step 2 的结果按以下结构整理:

markdown
## {日期范围} 任务进度报告

### 统计概览
| 状态 | 数量 |
|------|------|
| 未开始 | N |
| 进行中 | N |
| 已完成 | N |
| 已逾期 | N |
| **总计** | **N** |

### 逾期任务(需关注)
| 任务 | 负责人 | 截止日期 | 逾期天数 |
|------|--------|---------|---------|

### 进行中任务
| 任务 | 负责人 | 截止日期 | 进度 |
|------|--------|---------|------|

### 已完成任务
| 任务 | 完成时间 |
|------|---------|

统计规则:

  • 逾期 = 截止日期 < 当前时间 且 状态 != 已完成
  • 完成率 = 已完成 / 总计 × 100%
  • 逾期任务高亮显示(用红色标注)

Step 4: 推送报告(可选)

发送到飞书群

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

发送给个人

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

保存为文档

bash
lark-cli docs +create \
  --title "任务进度报告 ({日期})" \
  --markdown "{报告内容}" \
  --as user

降级策略

场景降级方案
无任务数据返回"当前没有符合条件的任务"
数据量过大只展示近 30 天数据,历史数据折叠
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

权限表

命令所需 scope
task +get-my-taskstask:task:readonly
im +messages-sendim:message
docs +createdocx:document:create

参考

Frequently asked questions

What does the Lark Workflow Task Digest AI skill do?

任务状态播报员:查询任务列表并按状态分类统计,生成进度报告,通过飞书消息推送或保存为文档。当用户需要'任务进度'、'任务报告'、'任务状态'、'待办汇报'、'项目进度报告'、'任务概览'、'今日任务完成情况'时使用。

Why use Lark Workflow Task Digest on TypingMind?

Because you install it once and use it with any model. Lark Workflow Task 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 Task 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-task-digest. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Task 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 Task Digest?

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

Is the Lark Workflow Task 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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