analyze-stock

Analyze Stock — One-Click Comprehensive Stock Analysis

Enter a company name or stock ticker, automatically collect data across five dimensions in parallel, and output a standard report after comprehensive analysis.

When to Use

Triggered when the user requests the following operations:

"Analyze Tencent" / "Analyze Kweichow Moutai" / "Analyze 600519"
"analyze NVDA" / "analyze Tesla"
"How is XX stock" / "Why has XX risen/fallen recently"
"Help me take a look at XX" / "Is XX worth buying"

Phase 0: Parse input

Based on the user input, identify the following information:

First determine that the directory containing this SKILL.md file is SKILL_DIR; if a script needs to be called, always use:

SKILL_DIR="<此 SKILL.md 文件所在目录的绝对路径>"

Company name and stock ticker
- If the user provided a ticker, use it directly
- If the user provided a company name, use WebSearch to look up the corresponding ticker
- A-share ticker format: 6-digit number (600519, 000858)
- US stock ticker format: English letters (NVDA, AAPL, TSLA)
Identify market type
- 6 pure digits → A-shares
- 4-5 digits or containing .HK suffix → Hong Kong stocks
- English letters → US stocks
- Company name contains Chinese and is a Chinese company → first search to confirm whether it is A-share or Hong Kong stock
- Other → US stocks
Determine key variables (all subsequent Agents need to use this)
- {ticker}: stock ticker
- {company_name}: full company name
- {company_name_en}: company English name (for US stocks)
- {market}: "A-shares" / "Hong Kong stocks" / "US stocks"
- {industry}: industry
- {website}: company official website URL

If the official website or industry cannot be determined, first use a WebSearch for a quick lookup;do not skip this step。

Phase 1: Parallel data collection (5 Subagents)

Key requirement: The following 5 Tasks must be placed inthe same messageto ensure parallel execution.

Each Agent uses subagent_type: "general-purpose"。

Agent 1: Stock price data and technical indicators

description: "Collect {ticker} stock price data"

prompt template:

你是股价数据分析师。请获取 {company_name}({ticker}) 近7个交易日的股价数据，并**重点突出最近1个交易日的变化**。

任务:
1. 使用 WebSearch 搜索 "{ticker} stock price last 7 days {market}" 获取最近行情
2. 使用 WebSearch 搜索 "{ticker} stock price today" 获取最新交易日详细数据
3. 整理以下信息:

   **最近1个交易日 (当日行情):**
   - 开盘价、最高价、最低价、收盘价
   - 当日涨跌幅与涨跌金额
   - 当日成交量与成交额，较前一日放量/缩量比例
   - 盘中关键时点走势 (如: 开盘冲高回落、尾盘拉升等)
   - 当日是否有影响股价的即时事件

   **近7个交易日汇总:**
   - 每日收盘价和涨跌幅
   - 7天累计涨跌幅
   - 成交量变化趋势 (放量/缩量)
   - 关键技术信号 (如有: 均线多空排列、RSI超买超卖、明显支撑位/阻力位)
   - 与大盘同期涨跌幅对比

输出要求:
- 以结构化格式返回，**当日行情单独列出**
- 重点突出: 当日涨跌幅、7天涨跌幅、量价配合情况、技术面关键信号
- 控制在 600 字以内
- 不要给出投资建议
- **标注数据来源**: 关键数据注明来源（如东方财富、雪球、Yahoo Finance、Bloomberg 等）及原始链接

If it is an A-share and akshare is installed, you may additionally instruct in the prompt:

如果可用，执行以下命令获取精确数据:
python "${SKILL_DIR}/scripts/data_fetcher.py" --code {ticker} --data-type valuation

Agent 2: News sentiment analysis

description: "Search for recent news about {company_name}"

prompt template:

你是财经新闻分析师。请搜索 {company_name}({ticker}) 最近7天的重要新闻和舆论。

任务:
1. 使用 WebSearch 搜索以下关键词 (至少搜2次不同关键词):
   - "{company_name} 最新新闻" 或 "{company_name} latest news"
   - "{ticker} 股票 本周" 或 "{ticker} stock this week"
2. 从搜索结果中挑选 3-5 条最重要的新闻
3. 使用 WebFetch 访问其中至少 2 条新闻的原文，验证内容真实性
4. 分析舆论整体倾向

输出要求:
- 列出 3-5 条关键新闻，每条包含: 日期、标题、来源名称、**原文URL**、简要内容(1-2句)
- 整体舆论倾向判断: 正面 / 负面 / 中性，并说明理由
- 识别是否有重大事件 (财报发布、政策变化、管理层变动、产品发布、诉讼等)
- 控制在 600 字以内

Agent 3: Industry comparison analysis

description: "Analyze the {industry} industry situation"

prompt template:

你是行业分析师。请分析 {company_name}({ticker}) 所在的 {industry} 行业近期情况。

任务:
1. 使用 WebSearch 搜索:
   - "{industry} 行业 近期趋势" 或 "{industry} industry trends"
   - "{company_name} 竞争对手" 或 "{company_name} competitors"
2. 整理以下信息:
   - 行业近期整体趋势 (上升/下行/平稳)
   - 影响行业的关键因素 (政策、技术、需求等)
   - 2-3 个主要竞争对手的近期股价表现
   - {company_name} 在行业中的大致地位

输出要求:
- 行业趋势概述 (2-3句)
- 竞争格局简表: 公司名、近7天涨跌、关键动态
- 该公司的相对优劣势 (1-2条)
- 控制在 500 字以内
- **标注数据来源**: 竞争对手数据注明来源（如 Bloomberg、Wind、东方财富等）及链接

Agent 4: Market environment analysis

description: "Analyze the current market environment"

prompt template:

你是宏观市场分析师。请分析当前全球市场环境，重点关注与 {company_name}({ticker}) 相关的市场因素。

任务:
1. 使用 WebSearch 搜索最新市场数据:
   - 主要指数近期走势:
     - 如果A股: "上证指数 深证成指 创业板指 本周"
     - 如果港股: "恒生指数 恒生科技指数 国企指数 本周"
     - 如果美股: "S&P 500 NASDAQ Dow Jones this week"
   - "VIX index today" (恐慌指数)
   - 近期重大宏观事件或央行动态
2. 评估:
   - 大盘趋势方向: 上涨 / 下跌 / 震荡
   - 市场情绪: Risk-on (追逐风险) / Risk-off (规避风险)
   - VIX 水平及含义
   - 是否有重大宏观事件影响

输出要求:
- 市场环境一句话总结
- 大盘指数近7天表现 (涨跌幅)
- VIX 水平和波动率判断
- 影响当前市场的 1-2 个关键因素
- 控制在 400 字以内
- **标注数据来源**: 各指标注明来源（如 CBOE、Yahoo Finance、MacroTrends 等）及链接

Agent 5: Company official website and announcement information

description: "Scrape information from {company_name}'s official website"

prompt template:

你是企业信息研究员。请获取 {company_name}({ticker}) 的官方最新动态。

任务:
1. 使用 WebFetch 访问公司官网: {website}
   - 查看首页是否有最新公告或新闻
2. 使用 WebSearch 搜索 "{company_name} 投资者关系" 或 "{company_name} investor relations"
   - 查找近期公告、财报摘要、业绩预告
3. 如果是上市公司，搜索最近的公告:
   - A股: "{company_name} 公告 巨潮资讯"
   - 港股: "{company_name} 公告 披露易" 或 "{company_name} hkex announcement"
   - 美股: "{company_name} SEC filing" 或 "{company_name} earnings"

输出要求:
- 公司最新官方动态 (产品、战略、人事等)
- 最近一次财报/业绩的关键数据 (如有)
- 近期重要公告摘要 (如有)
- 控制在 400 字以内
- 如果官网无法访问，说明情况并依赖搜索结果
- **标注数据来源**: 公告、财报数据注明来源（如 SEC EDGAR、巨潮资讯、港交所披露易等）及链接

Phase 2: Comprehensive analysis (main thread)

After waiting for all 5 Agents to return, complete the following analysis on the main thread.

Step 1: Information summary

Integrate the results of the 5 Agents to identify:

Between the dimensions, consistency signals (e.g., stock price rising + positive news + industry trending upward = strongly bullish)
Between the dimensions, contradictory signals (e.g., stock price rising but news negative = potential hidden risks)
At the same time, organize allsource URLsreturned by each Agent, and compile them into a source list for the "References" section at the end of the report

Step 2: Causal Attribution

Analyze the reasons for stock price changes, sorted by impact:

Direct drivers: company-level events (financial reports, announcements, products, public opinion)
Industry transmission factors: industry policies, changes in the competitive landscape
Market environment factors: overall market trends, liquidity, macroeconomic events

Pay special attention to the day's changes: Conduct a separate attribution analysis for the rise or fall over the most recent 1 trading day, distinguishing intraday drivers from medium-term trend factors.

Step 3: Trend Forecast

Based on the above analysis, provide:

Short-term outlook (1-2 weeks): considering technical signals + upcoming events
Medium-term outlook (1-3 months): considering fundamentals + industry trends
Main risk points: factors that may cause a trend reversal

Phase 3: Output Report

Output the final report in the following HTML format. Use inline CSS to ensure good display in both browsers and Feishu:

<!DOCTYPE html>
<html lang="zh-CN">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>{company_name} ({ticker}) 综合分析报告</title>
<style>
  body { font-family: -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; color: #1a1a1a; background: #f8f9fa; }
  .report { background: #fff; border-radius: 12px; padding: 32px; box-shadow: 0 2px 12px rgba(0,0,0,0.08); }
  h1 { font-size: 24px; border-bottom: 3px solid #1a73e8; padding-bottom: 12px; }
  h2 { font-size: 18px; color: #1a73e8; margin-top: 28px; border-left: 4px solid #1a73e8; padding-left: 10px; }
  h3 { font-size: 15px; color: #333; margin-top: 16px; }
  .meta { color: #666; font-size: 13px; margin-bottom: 16px; }
  .summary { background: #e8f0fe; border-radius: 8px; padding: 16px; font-size: 16px; font-weight: 500; margin: 16px 0; }
  .daily-highlight { background: #fff8e1; border: 1px solid #ffcc02; border-radius: 8px; padding: 16px; margin: 16px 0; }
  .daily-highlight h2 { color: #f57f17; border-left-color: #f57f17; }
  table { width: 100%; border-collapse: collapse; margin: 12px 0; font-size: 14px; }
  th { background: #f1f3f4; text-align: left; padding: 10px 12px; font-weight: 600; border-bottom: 2px solid #ddd; }
  td { padding: 8px 12px; border-bottom: 1px solid #eee; }
  tr:hover td { background: #f8f9fa; }
  .up { color: #d32f2f; font-weight: 600; }
  .down { color: #2e7d32; font-weight: 600; }
  .tag { display: inline-block; padding: 2px 8px; border-radius: 4px; font-size: 12px; font-weight: 500; }
  .tag-positive { background: #e8f5e9; color: #2e7d32; }
  .tag-negative { background: #ffebee; color: #c62828; }
  .tag-neutral { background: #f5f5f5; color: #616161; }
  .risk { background: #fff3e0; border-radius: 8px; padding: 12px 16px; margin: 8px 0; }
  .disclaimer { margin-top: 24px; padding-top: 16px; border-top: 1px solid #eee; color: #999; font-size: 12px; }
  .references { margin-top: 20px; padding: 16px; background: #f8f9fa; border-radius: 8px; border: 1px solid #e0e0e0; }
  .references h2 { font-size: 15px; color: #555; border-left-color: #9e9e9e; margin-top: 0; }
  .references ul { margin: 0; }
  .references li { font-size: 12px; color: #666; margin: 4px 0; }
  a.src { color: #1a73e8; text-decoration: none; }
  a.src:hover { text-decoration: underline; }
  ul, ol { padding-left: 20px; }
  li { margin: 6px 0; line-height: 1.6; }
</style>
</head>
<body>
<div class="report">

<h1>{company_name} ({ticker}) 综合分析报告</h1>
<div class="meta">分析日期: {date} | 分析周期: 近7个交易日 | 市场: {market}</div>

<div class="summary">{一句话总结}</div>

<!-- 当日行情 — 黄色高亮区块，放在最前面 -->
<div class="daily-highlight">
<h2>当日行情 ({最近交易日日期})</h2>
<table>
  <tr><th>指标</th><th>数值</th></tr>
  <tr><td>开盘价</td><td>...</td></tr>
  <tr><td>最高 / 最低</td><td>... / ...</td></tr>
  <tr><td>收盘价</td><td>...</td></tr>
  <tr><td>当日涨跌</td><td><span class="up/down">+/-X.XX%</span> (±金额)</td></tr>
  <tr><td>成交量</td><td>XXX万股 (较前日 +/-XX%)</td></tr>
  <tr><td>盘中走势</td><td>简述日内走势特征</td></tr>
</table>
<p><strong>当日变动归因:</strong> 简要说明当日涨跌的直接原因</p>
</div>

<h2>一、7日股价概览</h2>
<table>
  <tr><th>指标</th><th>数值</th></tr>
  <tr><td>当前价格</td><td>¥/$XXX</td></tr>
  <tr><td>7日涨跌幅</td><td><span class="up/down">+/-X.XX%</span></td></tr>
  <tr><td>同期大盘</td><td>+/-X.XX%</td></tr>
  <tr><td>成交量趋势</td><td>放量/缩量/持平</td></tr>
  <tr><td>技术面信号</td><td>...</td></tr>
</table>

<h2>二、股价变动原因分析</h2>
<h3>直接驱动因素</h3>
<ol><li>...</li></ol>
<h3>行业传导因素</h3>
<ol><li>...</li></ol>
<h3>市场环境因素</h3>
<ol><li>...</li></ol>

<h2>三、近期重要新闻</h2>
<table>
  <tr><th>日期</th><th>事件</th><th>来源</th><th>影响</th></tr>
  <tr><td>...</td><td>...</td><td><a href="原文URL" class="src">来源名称</a></td><td><span class="tag tag-positive/negative/neutral">利好/利空/中性</span></td></tr>
</table>
<p>舆论倾向: <strong>正面/负面/中性</strong></p>

<h2>四、行业对比</h2>
<table>
  <tr><th>公司</th><th>7日涨跌</th><th>关键动态</th></tr>
  <tr><td>{company_name}</td><td>...</td><td>...</td></tr>
  <tr><td>竞对A</td><td>...</td><td>...</td></tr>
  <tr><td>竞对B</td><td>...</td><td>...</td></tr>
</table>

<h2>五、市场环境</h2>
<ul>
  <li>大盘趋势: ...</li>
  <li>市场情绪: Risk-on / Risk-off</li>
  <li>VIX: XX (低/正常/高/极高 波动)</li>
  <li>关键宏观因素: ...</li>
</ul>

<h2>六、趋势展望</h2>
<h3>短期 (1-2周)</h3>
<ul><li>...</li></ul>
<h3>中期 (1-3月)</h3>
<ul><li>...</li></ul>
<h3>主要风险</h3>
<div class="risk">
<ol><li>...</li></ol>
</div>

<div class="references">
<h2>参考来源</h2>
<!-- 将 5 个 Agent 返回的所有 Sources 链接按维度分组列出，不得省略，不得使用占位符 -->
<ul>
  <li><strong>股价与技术面</strong>
    <ul>
      <li><a href="URL" class="src">标题 — 来源</a></li>
    </ul>
  </li>
  <li><strong>新闻舆论</strong>
    <ul>
      <li><a href="URL" class="src">标题 — 来源</a></li>
    </ul>
  </li>
  <li><strong>行业对比</strong>
    <ul>
      <li><a href="URL" class="src">标题 — 来源</a></li>
    </ul>
  </li>
  <li><strong>市场环境</strong>
    <ul>
      <li><a href="URL" class="src">标题 — 来源</a></li>
    </ul>
  </li>
  <li><strong>公司公告 / 官网</strong>
    <ul>
      <li><a href="URL" class="src">标题 — 来源</a></li>
    </ul>
  </li>
</ul>
</div>

<div class="disclaimer">声明: 本报告由 AI 自动生成，仅供参考，不构成任何投资建议。投资有风险，决策需谨慎。</div>
</div>
</body>
</html>

HTML filling rules:

When the price change is positive, use class="up"(red); when negative, use class="down"(green)
News impact labels: for positive use tag-positive, for negative use tag-negative, for neutral use tag-neutral
Replace the placeholders in the template with actual data and delete the comments
Ensure the HTML is complete and can be opened directly in a browser
The "References" section is required: Fill in all Sources links at the end of the results returned by the 5 Agents, grouped by dimension; each entry is in the format <a href="原始URL" class="src">标题 — 来源网站</a>; do not omit them, and do not keep placeholder URLs
The "Source" column of the news table must include clickable original article links (<a href="原文URL" class="src">来源名称</a>）

Save the report as a file: {company_name}-analysis-{date}.html, saved in the current working directory.

Phase 4: Generate PDF Report

Based on the HTML file generated in Phase 3, print the PDF via Chrome headless:

"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" \
  --headless --disable-gpu --no-sandbox \
  --print-to-pdf="$(pwd)/{company_name}-analysis-{date}.pdf" \
  --no-pdf-header-footer \
  "file://$(pwd)/{company_name}-analysis-{date}.html"

Note: If the system does not have Chrome, you can use npx -y md-to-pdf "$(pwd)/{company_name}-analysis-{date}.html" as an alternative.

Finally output two files:

./{company_name}-analysis-{date}.html — HTML version (can be opened in a browser)
./{company_name}-analysis-{date}.pdf — PDF version (ready to share)

Error Handling

Agent timeout or failure: If an Agent does not return a result, mark that dimension as "data missing" in the report, and analyze the other dimensions as usual
Stock ticker cannot be identified: Prompt the user to confirm the ticker or company name
akshare not installed (A-shares): fall back to obtaining data purely via WebSearch
Official website inaccessible: Skip official website scraping and rely on search engine results

Notes

The output of each Agent is strictly limited in word count to prevent main thread context overflow.
The comprehensive analysis stage focuses on cross-correlation, rather than simple listing.
Causal analysis should distinguish between "correlation" and "causation".
Trend predictions must clearly indicate uncertainty.

About this skill

When to Use

Phase 0: Parse input

Phase 1: Parallel data collection (5 Subagents)

Agent 1: Stock price data and technical indicators

Agent 2: News sentiment analysis

Agent 3: Industry comparison analysis

Agent 4: Market environment analysis

Agent 5: Company official website and announcement information

Phase 2: Comprehensive analysis (main thread)

Step 1: Information summary

Step 2: Causal Attribution

Step 3: Trend Forecast

Phase 3: Output Report

Phase 4: Generate PDF Report

Error Handling

Notes