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Lark Workflow Health Diary

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liangdabiao
lark-workflow-health-diary

健康日记:通过飞书消息发送食物照片,AI识别食物并记录,追踪饮食和身体状态关联,每周生成健康报告。当用户需要'记录饮食'、'健康日记'、'吃了什么'、'身体不舒服'、'健康追踪'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-health-diary
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 Health Diary 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-health-diary
  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-health-diary .claude/skills/lark-workflow-health-diary
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Health Diary 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 Health Diary 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 Health Diary 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.

健康日记工作流

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

  • "帮我记录今天的午餐" / "记录饮食"
  • "我今天胃不舒服" / "昨晚没睡好"
  • "这周吃了什么?" / "饮食回顾"
  • "帮我分析一下饮食和身体状态的关联"
  • "生成健康周报"

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,base,docs

工作流总览

记录饮食:
  用户发照片/描述 ──► AI 识别食物 ──► base +record-upsert

记录身体状态:
  用户描述状态 ──► base +record-upsert

关联分析:
  base +data-query ──► AI 分析饮食-状态关联

周报生成:
  base +record-list ──► AI 汇总 ──► doc +create

主动提醒:
  base +record-list ──► 识别风险食物 ──► im +messages-send

数据初始化

Base 表结构

表 1:饮食记录 (diet_log)

字段名类型说明
日期日期记录日期
餐次单选早餐/午餐/晚餐/加餐/夜宵
时间日期具体时间
食物列表文本识别出的食物名称(逗号分隔)
食物详情文本每种食物的详细描述和份量
照片链接URL飞书中食物照片的链接
估算热量数字估算的卡路里(可选,AI估算)
备注文本用户补充说明
创建时间日期记录创建时间

表 2:身体状态 (body_status)

字段名类型说明
日期日期状态日期
时间日期具体时间
状态类型单选胃部不适/头痛/失眠/疲劳/过敏/皮肤/情绪/其他
严重程度单选轻微/中等/严重
状态描述文本详细描述
可能诱因文本用户猜测的原因(如有)
持续时间文本症状持续时长
缓解方式文本如何缓解的(如有)
创建时间日期记录创建时间

表 3:关联分析 (correlation)

字段名类型说明
食物文本可疑食物名称
状态文本关联的身体状态
出现次数数字该组合出现的次数
时间差模式文本食用后多久出现症状(如"2-4小时")
关联强度单选低/中/高
最近出现日期日期最近一次出现时间
备注文本分析备注

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 "饮食记录" --fields '[
  {"name":"日期","type":"datetime"},
  {"name":"餐次","type":"select","multiple":false,"options":[{"name":"早餐"},{"name":"午餐"},{"name":"晚餐"},{"name":"加餐"},{"name":"夜宵"}]},
  {"name":"时间","type":"datetime"},
  {"name":"食物列表","type":"text"},
  {"name":"食物详情","type":"text"},
  {"name":"照片链接","type":"link"},
  {"name":"估算热量","type":"number"},
  {"name":"备注","type":"text"},
  {"name":"创建时间","type":"datetime"}
]'
lark-cli base +table-create --name "身体状态" --fields '[
  {"name":"日期","type":"datetime"},
  {"name":"时间","type":"datetime"},
  {"name":"状态类型","type":"select","multiple":false,"options":[{"name":"胃部不适"},{"name":"头痛"},{"name":"失眠"},{"name":"疲劳"},{"name":"过敏"},{"name":"皮肤"},{"name":"情绪"},{"name":"其他"}]},
  {"name":"严重程度","type":"select","multiple":false,"options":[{"name":"轻微"},{"name":"中等"},{"name":"严重"}]},
  {"name":"状态描述","type":"text"},
  {"name":"可能诱因","type":"text"},
  {"name":"持续时间","type":"text"},
  {"name":"缓解方式","type":"text"},
  {"name":"创建时间","type":"datetime"}
]'
lark-cli base +table-create --name "关联分析" --fields '[
  {"name":"食物","type":"text"},
  {"name":"状态","type":"text"},
  {"name":"出现次数","type":"number"},
  {"name":"时间差模式","type":"text"},
  {"name":"关联强度","type":"select","multiple":false,"options":[{"name":"低"},{"name":"中"},{"name":"高"}]},
  {"name":"最近出现日期","type":"datetime"},
  {"name":"备注","type":"text"}
]'

模式一:记录饮食

方式 A:照片识别

用户通过飞书消息发送食物照片:

bash
# 获取图片消息
lark-cli im +messages-resources-download --message-id "<message_id>" --file-key "<file_key>"

AI 处理:

  1. 使用多模态能力识别照片中的食物
  2. 提示用户确认或补充:"收到,晚餐是肉酱披萨,吃了几片?"
  3. 用户回复后,记录到饮食记录表

方式 B:文字描述

用户直接描述吃了什么:

  • "午餐吃了米饭、红烧肉和青菜"
  • "下午喝了一杯拿铁"

AI 处理:

  1. 解析食物名称
  2. 如有模糊描述,追问细节(如"咖啡加了什么?全脂牛奶还是燕麦奶?")
  3. 估算热量(可选)
  4. 记录到饮食记录表

记录命令

bash
lark-cli base +record-upsert --table-id "<diet_table_id>" --json '{
  "日期": "2026-03-31",
  "餐次": "午餐",
  "时间": "2026-03-31 12:30:00",
  "食物列表": "米饭,红烧肉,青菜",
  "食物详情": "米饭一碗、红烧肉约200g、炒青菜一份",
  "备注": ""
}'

模式二:记录身体状态

用户描述身体状态:

  • "今天胃不舒服" → 记录:状态类型=胃部不适
  • "昨晚失眠了,大概凌晨3点才睡着" → 记录:状态类型=失眠
  • "吃完午饭头有点晕" → 记录:状态类型=头痛 + 可能诱因=午餐
bash
lark-cli base +record-upsert --table-id "<body_table_id>" --json '{
  "日期": "2026-03-31",
  "时间": "2026-03-31 14:00:00",
  "状态类型": "胃部不适",
  "严重程度": "中等",
  "状态描述": "午饭后胃有点胀,持续约1小时",
  "可能诱因": "午餐吃的太快"
}'

模式三:关联分析

AI 自动分析饮食和身体状态之间的关联:

分析逻辑

  1. 时间窗口匹配:查找身体状态出现前 2-24 小时内的饮食记录
  2. 频率统计:统计每种食物与每种身体状态的共现频率
  3. 时间差模式:分析食物食用到症状出现的时间间隔规律
  4. 排除常见因素:排除普遍出现的食物(如米饭、水),聚焦特异性食物

分析命令

bash
# 获取最近 30 天的数据
lark-cli base +record-list --table-id "<diet_table_id>" --filter '...日期 >= "30天前"'
lark-cli base +record-list --table-id "<body_table_id>" --filter '...日期 >= "30天前"'

AI 分析输出

## 饮食-身体状态关联分析(过去 30 天)

### 发现的关联模式
| 食物 | 身体状态 | 出现次数 | 典型时间差 | 关联强度 |
|------|---------|---------|-----------|---------|
| 冰美式 | 胃部不适 | 3次 | 1-2小时 | 高 |
| 辣味火锅 | 失眠 | 2次 | 6-8小时 | 中 |

### 建议
- 冰美式可能与你的胃部不适有较强关联,建议减少摄入或改用热饮
- 辣味火锅后出现失眠,建议避免晚餐食用辛辣食物

更新关联分析表

bash
lark-cli base +record-upsert --table-id "<correlation_table_id>" --json '{
  "食物": "冰美式",
  "状态": "胃部不适",
  "出现次数": 3,
  "时间差模式": "1-2小时",
  "关联强度": "高"
}'

模式四:主动提醒

当用户记录身体状态时,AI 主动检查关联分析表:

bash
lark-cli base +record-list --table-id "<correlation_table_id>" --filter '...关联强度 IN ("高","中")'

如果当前饮食包含高风险食物,立即提醒:

提醒:你今天喝了冰美式,之前 3 次喝冰美式后 1-2 小时内都出现了胃部不适。
建议:注意观察,如果出现不适请记录。

模式五:周报生成

每周生成健康报告并保存为飞书文档:

报告内容

## 健康周报({日期范围})

### 饮食概览
- 记录天数:N 天
- 总餐次:N 次
- 饮食规律性:{评价}

### 身体状态概览
- 记录不适:N 次
- 主要症状:{列表}
- 严重程度分布:轻微 N 次、中等 N 次、严重 N 次

### 本周新发现
- {新发现的关联模式}

### 趋势对比(vs 上周)
- 饮食多样性:{变化}
- 不适频率:{变化}

### 建议
- {个性化建议}

生成命令

bash
lark-cli doc +create --title "健康周报 ({日期范围})" --markdown "<报告内容>"

权限表

命令所需 scope
im +chat-messages-listim:message:readonly
im +messages-resources-downloadim:message.resource:readonly
im +messages-sendim:message:send_as_bot
base +table-createbitable:app
base +record-listbitable:app:read
base +record-upsertbitable:app:write
base +data-querybitable:app:read
docs +createdocx:document:create

参考

  • lark-shared — 认证、权限(必读)
  • lark-im — +chat-messages-list、+messages-resources-download、+messages-send 详细用法
  • lark-base — +table-create、+record-list、+record-upsert、+data-query 详细用法
  • lark-doc — +create 详细用法

Frequently asked questions

What does the Lark Workflow Health Diary AI skill do?

健康日记:通过飞书消息发送食物照片,AI识别食物并记录,追踪饮食和身体状态关联,每周生成健康报告。当用户需要'记录饮食'、'健康日记'、'吃了什么'、'身体不舒服'、'健康追踪'时使用。

Why use Lark Workflow Health Diary on TypingMind?

Because you install it once and use it with any model. Lark Workflow Health Diary 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 Health Diary 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-health-diary. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Health Diary?

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 Health Diary?

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

Is the Lark Workflow Health Diary 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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