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Lark Workflow Stock Analyzer

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liangdabiao
lark-workflow-stock-analyzer

股票研究助手:四层架构(自选股监控/财报提醒/深度研究/自动选股),多维表格管理持仓和自选股,AI多角色并行分析生成报告。当用户需要'股票分析'、'自选股'、'研究XX股票'、'持仓快照'、'财报分析'、'选股'时使用。

Overview

Publisherliangdabiao
Repositorylark-workflow-feishu-cli
Skill namelark-workflow-stock-analyzer
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 Stock Analyzer 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-stock-analyzer
  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-stock-analyzer .claude/skills/lark-workflow-stock-analyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lark Workflow Stock Analyzer 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 Stock Analyzer 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 Stock Analyzer 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.

股票研究助手工作流

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

  • "帮我分析一下苹果的股票" / "研究一下 AAPL"
  • "我的自选股有哪些?" / "添加/删除自选股"
  • "生成持仓快照" / "今天赚了多少"
  • "这周有哪些财报发布?" / "分析财报"
  • "帮我选几只好股票" / "技术面选股"

前置条件

仅支持 user 身份。执行前确保已授权:

bash
lark-cli auth login --domain base,docs,im,sheets

工作流总览

四层架构:

第一层:自选股监控
  base +record-list ──► 检查目标价 ──► WebSearch获取行情 ──► im +messages-send(到达通知)

第二层:财报提醒
  WebSearch ──► 查询财报日程 ──► 到期时自动分析 ──► doc +create(财报摘要)

第三层:深度研究
  启动 4 个 AI 分析师并行 ──► 各自分析 ──► 汇总报告 ──► doc +create

第四层:自动选股
  用户设定条件 ──► WebSearch 全市场扫描 ──► 筛选匹配 ──► 报告

数据初始化

Base 表结构

表 1:自选股 (watchlist)

字段名类型说明
股票代码文本如 AAPL、600519.SH
股票名称文本如 Apple、贵州茅台
市场单选美股/A股/港股/其他
目标价数字用户设定的目标价格
目标方向单选看涨(到达买入)/看跌(到达卖出)
当前价数字最新行情价格(定期更新)
上次更新日期价格最后更新时间
备注文本自由备注
加入时间日期加入自选股的时间

表 2:持仓 (portfolio)

字段名类型说明
股票代码文本如 AAPL
股票名称文本如 Apple
买入价数字平均买入价格
持仓数量数字持有股数
买入日期日期建仓日期
当前价数字最新价格
市值公式持仓数量 × 当前价
盈亏金额公式市值 - (买入价 × 持仓数量)
盈亏比例公式盈亏金额 / (买入价 × 持仓数量)

表 3:分析记录 (analysis_log)

字段名类型说明
股票代码文本如 AAPL
分析类型单选深度研究/财报分析/日常监控/选股结果
分析日期日期分析时间
结论单选强烈看多/看多/中性/看空/强烈看空
信心评分数字1-10
看多理由文本主要看多论点
看空理由文本主要看空论点
建议文本操作建议
报告链接URL详细分析报告的飞书文档链接

表 4:财报日程 (earnings_calendar)

字段名类型说明
股票代码文本
股票名称文本
财报日期日期预计发布日期
财报类型单选季报/年报
已分析单选否/已分析
分析链接URL财报分析报告链接

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 "自选股" --fields '[
  {"name":"股票代码","type":"text"},
  {"name":"股票名称","type":"text"},
  {"name":"市场","type":"select","multiple":false,"options":[{"name":"美股"},{"name":"A股"},{"name":"港股"},{"name":"其他"}]},
  {"name":"目标价","type":"number"},
  {"name":"目标方向","type":"select","multiple":false,"options":[{"name":"看涨(到达买入)"},{"name":"看跌(到达卖出)"}]},
  {"name":"当前价","type":"number"},
  {"name":"上次更新","type":"datetime"},
  {"name":"备注","type":"text"},
  {"name":"加入时间","type":"datetime"}
]'
lark-cli base +table-create --name "持仓" --fields '[
  {"name":"股票代码","type":"text"},
  {"name":"股票名称","type":"text"},
  {"name":"买入价","type":"number"},
  {"name":"持仓数量","type":"number"},
  {"name":"买入日期","type":"datetime"},
  {"name":"当前价","type":"number"}
]'
# 注:市值、盈亏金额、盈亏比例 为公式字段,需单独用 +field-create 创建(type="formula" 需要引用其他字段 ID)
lark-cli base +table-create --name "分析记录" --fields '[
  {"name":"股票代码","type":"text"},
  {"name":"分析类型","type":"select","multiple":false,"options":[{"name":"深度研究"},{"name":"财报分析"},{"name":"日常监控"},{"name":"选股结果"}]},
  {"name":"分析日期","type":"datetime"},
  {"name":"结论","type":"select","multiple":false,"options":[{"name":"强烈看多"},{"name":"看多"},{"name":"中性"},{"name":"看空"},{"name":"强烈看空"}]},
  {"name":"信心评分","type":"number"},
  {"name":"看多理由","type":"text"},
  {"name":"看空理由","type":"text"},
  {"name":"建议","type":"text"},
  {"name":"报告链接","type":"link"}
]'
lark-cli base +table-create --name "财报日程" --fields '[
  {"name":"股票代码","type":"text"},
  {"name":"股票名称","type":"text"},
  {"name":"财报日期","type":"datetime"},
  {"name":"财报类型","type":"select","multiple":false,"options":[{"name":"季报"},{"name":"年报"}]},
  {"name":"已分析","type":"select","multiple":false,"options":[{"name":"否"},{"name":"已分析"}]},
  {"name":"分析链接","type":"link"}
]'

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

第一层:自选股监控

管理自选股

bash
# 添加自选股
lark-cli base +record-upsert --table-id "<watchlist_table_id>" --json '{
  "股票代码": "AAPL",
  "股票名称": "Apple",
  "市场": "美股",
  "目标价": 200,
  "目标方向": "看涨"
}'

# 查看自选股列表
lark-cli base +record-list --table-id "<watchlist_table_id>"

价格检查

通过 AI WebSearch 获取最新行情:

AI 处理:

  1. 获取自选股列表
  2. 逐一搜索最新股价
  3. 对比目标价,判断是否触发
  4. 触发时发送飞书消息通知

通知格式

目标价到达通知!

{股票名称}({股票代码})
当前价格:${current_price}
目标价格:${target_price}
目标方向:{看涨/看跌}

第二层:财报提醒

查询财报日程

bash
# 获取自选股列表
lark-cli base +record-list --table-id "<watchlist_table_id>"

AI 处理:

  1. 通过 WebSearch 查询自选股中各公司的财报发布日期
  2. 记录到财报日程表
  3. 到发布日时自动触发财报分析

财报分析

当财报发布时:

AI 财报分析师处理:
1. 搜索最新财报数据(营收、利润、EPS、同比增长等)
2. 提取管理层关键表态
3. 分析市场反应(盘后/次日股价变动)
4. 与分析师预期对比(beat/miss)
5. 生成通俗易懂的财报摘要

财报摘要格式

## {公司名称}({股票代码}){季度}财报摘要

### 核心数据
| 指标 | 实际值 | 预期值 | 变化 |
|------|--------|--------|------|
| 营收 | $XX亿 | $XX亿 | +X% |
| EPS | $X.XX | $X.XX | +X% |

### 管理层说了什么
- {要点1}
- {要点2}

### 市场反应
- 盘后涨跌:{幅度}
- 分析师评价:{摘要}

### 综合评价
{总结性判断}

保存为飞书文档:

bash
lark-cli docs +create --title "财报摘要:{公司} {季度}" --markdown "<内容>"

第三层:深度研究

启动多角色分析

对指定股票启动 4 个 AI 分析师并行工作:

分析师 1:基本面分析师

  • 营收、利润增长趋势
  • 毛利率、净利率变化
  • 自由现金流
  • 债务水平

分析师 2:护城河分析师

  • 品牌价值
  • 技术壁垒/专利
  • 网络效应
  • 转换成本
  • 规模优势

分析师 3:估值分析师

  • P/E、P/B、P/S 等相对估值
  • DCF 绝对估值
  • 与同行业对比
  • 历史估值区间

分析师 4:风险分析师

  • 业务风险
  • 竞争风险
  • 监管风险
  • 宏观风险
  • 技术颠覆风险

汇总报告

## {公司名称}({股票代码})深度研究报告

### 总览
| 维度 | 评分(1-10) | 摘要 |
|------|-----------|------|
| 基本面 | {分} | {一句话} |
| 护城河 | {分} | {一句话} |
| 估值 | {分} | {一句话} |
| 风险 | {分} | {一句话} |

### 看多理由
1. {理由}
2. {理由}
3. {理由}

### 看空理由
1. {理由}
2. {理由}
3. {理由}

### 信心评分:{分}/10
### 建议:{操作建议}

保存报告:

bash
lark-cli docs +create --title "深度研究:{公司}" --markdown "<报告>"
lark-cli base +record-upsert --table-id "<analysis_table_id>" --json '{"字段名":"值"}'

第四层:自动选股

用户设定条件

用户描述选股条件:

  • "帮我找技术面超卖的蓝筹股"
  • "ROE 大于 15%、PE 小于 20 的公司"
  • "最近一个月涨幅最大的科技股"

AI 执行选股

1. 将自然语言条件转化为可执行的搜索策略
2. WebSearch 获取市场数据
3. 根据条件筛选匹配
4. 生成选股结果报告

结果报告

## 选股结果

### 筛选条件
{原始条件 + 转化后的可执行条件}

### 符合条件的股票
| 排名 | 股票 | 价格 | 涨跌幅 | 关键指标 |
|------|------|------|--------|---------|
| 1 | {股票} | ${价} | {涨跌}% | {指标} |

### 说明
{筛选逻辑说明和数据来源}

持仓快照

定期生成持仓概览:

bash
lark-cli base +record-list --table-id "<portfolio_table_id>"

AI 计算汇总:

  • 总市值
  • 总盈亏金额和比例
  • 最佳/最差表现股票
  • 持仓分布

权限表

命令所需 scope
base +table-createbitable:app
base +record-listbitable:app:read
base +record-upsertbitable:app:write
docs +createdocx:document:create
im +messages-sendim:message:send_as_bot
sheets +writesheets:spreadsheet:write

参考

Frequently asked questions

What does the Lark Workflow Stock Analyzer AI skill do?

股票研究助手:四层架构(自选股监控/财报提醒/深度研究/自动选股),多维表格管理持仓和自选股,AI多角色并行分析生成报告。当用户需要'股票分析'、'自选股'、'研究XX股票'、'持仓快照'、'财报分析'、'选股'时使用。

Why use Lark Workflow Stock Analyzer on TypingMind?

Because you install it once and use it with any model. Lark Workflow Stock Analyzer 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 Stock Analyzer 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-stock-analyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lark Workflow Stock Analyzer?

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 Stock Analyzer?

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

Is the Lark Workflow Stock Analyzer 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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