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Lark Workflow Content Pipeline

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liangdabiao
lark-workflow-content-pipeline

内容创作管道:在飞书群聊中标记内容创意,自动研究话题、检查重复选题、评估价值、生成完整方案,创建飞书任务跟踪。当用户需要'内容创意'、'灵感记录'、'选题评估'、'内容方案'、'创意管道'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-content-pipeline
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 Content Pipeline 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-content-pipeline
  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-content-pipeline .claude/skills/lark-workflow-content-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Content Pipeline 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 Content Pipeline 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 Content Pipeline 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.

内容创作管道工作流

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

  • "这是一个内容创意" / "标记为内容创意"
  • "帮我评估这个选题" / "选题评估"
  • "研究一下这个话题" / "话题调研"
  • "生成内容方案" / "写个大纲"
  • "我的创意库有哪些?" / "创意列表"

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,task,wiki,base,docs

工作流总览

创意触发:
  用户标记消息/直接描述 ──► AI 读取上下文

话题研究:
  ├── WebSearch ──────────► 搜索最新动态
  ├── doc +search ─────────► 检查知识库重复选题
  └── base +record-list ───► 检查创意库重复

评估与方案:
  AI 评估价值 ──► 生成方案(标题/大纲/开头)

任务创建:
  task +create ──► 创建飞书任务跟踪
  base +record-upsert ──► 存入创意库

通知:
  im +messages-send ──► 回复完成通知

数据初始化

Base 表结构

表 1:创意库 (idea_library)

字段名类型说明
创意标题文本创意的简短标题
来源单选群聊/对话/手动/其他
来源详情文本来源群聊名/对话上下文
原始描述文本创意的原始描述
话题领域单选科技/商业/AI/产品/其他
评估分数数字1-10 分(AI评估+用户调整)
状态单选灵感/研究中/方案/执行中/已完成/已放弃
重复检查单选通过/疑似重复/重复
重复来源文本如果重复,指向已有内容
关联任务ID文本飞书任务 task_id
方案文档链接URL生成的方案文档链接
创建时间日期入库时间
更新时间日期最后更新时间

表 2:创意研究 (idea_research)

字段名类型说明
关联创意关联关联到「创意库」
研究来源文本搜索来源 URL 或描述
研究类型单选最新动态/竞品分析/用户讨论/数据支持
摘要文本研究内容摘要
相关性单选高/中/低

初始化命令

bash
# 注意:+table-create 可能部分成功,需捕获错误后用 +field-create 补字段
# 注意:连续创建字段会触发限流(错误码 800004135),每次调用间隔至少 1 秒
lark-cli base +table-create --name "创意库" --fields '[
  {"name":"创意标题","type":"text"},
  {"name":"来源","type":"select","multiple":false,"options":[{"name":"群聊"},{"name":"对话"},{"name":"手动"},{"name":"其他"}]},
  {"name":"来源详情","type":"text"},
  {"name":"原始描述","type":"text"},
  {"name":"话题领域","type":"select","multiple":false,"options":[{"name":"科技"},{"name":"商业"},{"name":"AI"},{"name":"产品"},{"name":"其他"}]},
  {"name":"评估分数","type":"number"},
  {"name":"状态","type":"select","multiple":false,"options":[{"name":"灵感"},{"name":"研究中"},{"name":"方案"},{"name":"执行中"},{"name":"已完成"},{"name":"已放弃"}]},
  {"name":"重复检查","type":"select","multiple":false,"options":[{"name":"通过"},{"name":"疑似重复"},{"name":"重复"}]},
  {"name":"重复来源","type":"text"},
  {"name":"关联任务ID","type":"text"},
  {"name":"方案文档链接","type":"link"},
  {"name":"创建时间","type":"datetime"},
  {"name":"更新时间","type":"datetime"}
]'
lark-cli base +table-create --name "创意研究" --fields '[
  {"name":"研究来源","type":"text"},
  {"name":"研究类型","type":"select","multiple":false,"options":[{"name":"最新动态"},{"name":"竞品分析"},{"name":"用户讨论"},{"name":"数据支持"}]},
  {"name":"摘要","type":"text"},
  {"name":"相关性","type":"select","multiple":false,"options":[{"name":"高"},{"name":"中"},{"name":"低"}]}
]'
# 注:关联创意字段需单独用 +field-create 创建(type="lookup" 需要关联目标表 ID)

限流处理:如果 +table-create 因限流部分失败,表仍会创建成功。此时:

  1. 用 base +table-list 获取 table_id
  2. 用 base +field-create --table-id <id> --json '{"name":"字段名","type":"text"}' 逐个补字段,每次间隔 1-2 秒

模式一:创意触发与处理

方式 A:从飞书群聊标记

用户在群聊中标记某条消息为"内容创意":

bash
# 获取群聊消息及上下文
lark-cli im +chat-messages-list --chat-id "<chat_id>" --count 20

AI 读取消息上下文,理解创意的完整背景。

方式 B:直接描述

用户在对话中直接描述创意:

  • "我觉得可以写一篇关于 AI Agent 在企业落地的文章"
  • "这个话题不错,帮我做成选题"

处理流程

创意触发 ──► AI 理解背景 ──► 提炼核心创意
                                  │
                                  ▼
                          进入研究流程

模式二:话题研究

Step 1: 搜索最新动态

使用 AI 能力搜索相关话题的最新动态:

  • 搜索引擎搜索:最新文章、讨论、趋势
  • 技术社区:相关技术进展

Step 2: 检查重复选题

检查创意库:

bash
lark-cli base +record-list --table-id "<idea_table_id>" --filter '...话题领域 = "<领域>"'

检查知识库(联动知识库 Skill):

bash
lark-cli docs +search --query "<话题关键词>"

Step 3: 评估话题热度

AI 基于搜索结果评估:

  • 该话题最近的讨论热度
  • 是否有时效性(热点事件)
  • 竞争对手是否已覆盖类似话题
  • 目标受众的兴趣度

Step 4: 记录研究结果

bash
lark-cli base +record-upsert --table-id "<research_table_id>" --json '{"字段名":"值"}'

模式三:方案生成

AI 评估创意价值

评估维度(每项 1-10 分):

维度权重说明
时效性25%是否是当下热点
差异化25%与已有内容的差异程度
受众价值20%对目标受众的价值
可执行性15%创意的可执行程度
延展性15%是否可以做成系列

生成内容方案

## 内容方案

### 基本信息
- 标题建议:{3个备选标题}
- 内容类型:{文章/视频/播客/系列}
- 目标字数:{建议字数}
- 预计耗时:{预估}

### 开头建议(Hook)
> {一个能抓住读者注意力的开头段落}

### 详细大纲
1. {第一章标题}
   - {要点1}
   - {要点2}
2. {第二章标题}
   - {要点1}
   - {要点2}
...

### 关键数据/引用
- {支撑论点的数据和引用}

### 发布建议
- 推荐平台:{平台列表}
- 最佳发布时间:{时间建议}
- 标签建议:{标签列表}

### 评估分数:{总分}/10
- 时效性:{分} | 差异化:{分} | 受众价值:{分}
- 可执行性:{分} | 延展性:{分}

保存方案为飞书文档

bash
lark-cli docs +create --title "内容方案:{创意标题}" --markdown "<方案内容>"

模式四:任务创建

用户确认方案后,自动创建飞书任务跟踪:

bash
lark-cli task +create --summary "创作:{创意标题}" --description "内容方案已生成,详见文档链接" --due "<建议完成日>"

更新创意库状态:

bash
lark-cli base +record-upsert --table-id "<idea_table_id>" --json '{
  "创意标题": "...",
  "状态": "执行中",
  "关联任务ID": "...",
  "方案文档链接": "..."
}'

模式五:创意库管理

查看创意列表

bash
lark-cli base +record-list --table-id "<idea_table_id>" --filter '...状态 != "已完成" AND 状态 != "已放弃"' --sort '评估分数 DESC'

更新创意状态

  • "把这个创意标记为已完成" → 更新状态为"已完成"
  • "放弃这个创意" → 更新状态为"已放弃"
  • "调整评估分数" → 更新评估分数

权限表

命令所需 scope
im +chat-messages-listim:message:readonly
im +messages-sendim:message:send_as_bot
task +createtask:task:write
docs +createdocx:document:create
docs +searchdrive:drive:read
base +table-createbitable:app
base +record-listbitable:app:read
base +record-upsertbitable:app:write

参考

Frequently asked questions

What does the Lark Workflow Content Pipeline AI skill do?

内容创作管道:在飞书群聊中标记内容创意,自动研究话题、检查重复选题、评估价值、生成完整方案,创建飞书任务跟踪。当用户需要'内容创意'、'灵感记录'、'选题评估'、'内容方案'、'创意管道'时使用。

Why use Lark Workflow Content Pipeline on TypingMind?

Because you install it once and use it with any model. Lark Workflow Content Pipeline 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 Content Pipeline 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-content-pipeline. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Content Pipeline?

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 Content Pipeline?

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

Is the Lark Workflow Content Pipeline 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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