| name | stock |
| description | Query stock quotes for US stocks, Hong Kong stocks, and A-shares. Use when the user asks about a stock price, market quote, or stock performance — including phrases like "查一下 BABA", "BABA 现在多少", "阿里巴巴股价", "茅台今天", "港股 9988", "美股行情", or any mention of a specific stock ticker or company name wanting price/change data. Also trigger for market overview requests like "大盘怎么样", "美股今天", "港股行情".
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Stock Quote Skill
Fetch latest stock price, change, and anomaly detection for US stocks, HK stocks, and A-shares.
Uses stooq.com as data source — reliable, no auth, bypasses system proxy automatically.
Data is T+1 for all markets (previous trading day close). This is expected and normal.
Step 1 — Identify Stock & Market
Parse the user's query:
- A-share: 6-digit code (000001, 600519, 300750) or Chinese company name → market =
cn
- HK stock: 4–5 digit code (9988, 00700, 9999) or
XXXX.HK format → market = hk
- US stock: Latin ticker (BABA, AAPL, TSLA, NVDA) or
BABA.US format → market = us
For well-known companies, infer the market:
- 阿里巴巴 → BABA (US) + 09988 (HK) — user usually means one, ask if unclear
- 腾讯 → 00700 (HK)
- 茅台/贵州茅台 → 600519 (A-share)
- 宁德时代 → 300750 (A-share)
For HK symbols, pass as-is (no zero-padding): 9988, 700. stooq handles it.
Step 2 — Fetch Data
Use the unified fetch_stock.py script. Market codes: hk, cn, us.
python3 /Users/henry/.agentara/.claude/skills/stock/fetch_stock.py hk 9988
python3 /Users/henry/.agentara/.claude/skills/stock/fetch_stock.py cn 600519
python3 /Users/henry/.agentara/.claude/skills/stock/fetch_stock.py us BABA
Output is a JSON array of rows: {date, open, close, high, low, vol, pct, chg}.
If the output starts with {"error":, report the error to the user and stop.
Network notes: stooq.com is called with trust_env=False (bypasses Clash/system proxy).
No external Python deps beyond requests (stdlib-equivalent, always available).
Stooq symbol format:
- HK:
9988.hk (no leading zeros)
- CN:
600519.cn
- US:
baba.us (lowercase)
Step 3 — Analyze & Detect Anomalies
From the JSON output (up to 21 rows):
- Latest row = most recent trading day data
- Latest price =
close of last row
- Latest change% =
pct of last row
- Avg volatility = mean of
|pct| of rows 2–21 (excluding last row)
- Avg volume = mean of
vol of rows 2–6 (5-day avg, excluding last)
- Latest volume =
vol of last row
Anomaly flags (report any that apply):
|pct| > 5% → 🚨 大幅波动|pct| > 2% AND |pct| > 2 × avg_volatility → ⚠️ 异常波动（超出近期均值2倍）latest_vol > 2 × avg_volume → 📊 成交量异常放大（X倍）latest_vol < 0.4 × avg_volume → 🔇 成交量异常萎缩
If US stock pct is always near 0 except the last row, it's because the diff is computed on fetched data — use the last row's pct directly.
Step 4 — Format Output
Keep it brief and mobile-friendly. Use list format, no tables.
Color + emoji rules (Chinese convention: 红涨绿跌):
pct > 0 → emoji 📈, wrap numbers in <font color='red'>...</font>pct < 0 → emoji 📉, wrap numbers in <font color='green'>...</font>pct == 0 → emoji ➡️, no color wrapper
Template (adapt to context):
**{公司名} · {市场} {代码}**
- 最新价：<font color='red/green'>**{price} {货币}**</font>
- 涨跌：{emoji} <font color='red/green'>**{chg:+.2f} / {pct:+.2f}%**</font>
- 最新交易日：{date}
{anomaly_section_if_any}
Anomaly section (only if flags exist):
⚠️ 异常提示
- [flag 1]
- [flag 2]
近5日均幅 **{avg_vol:.2f}%**，今日 <font color='red/green'>**{pct:.2f}%**</font>
If no anomaly: omit the anomaly section entirely. Keep the whole response under 10 lines.
Step 5 — Currency
- A-share: ¥ (人民币)
- HK: HK$ (港元)
- US: $ (美元)
Notes
- US data lags by 1 trading day (Sina source) — this is expected, tell the user if they ask
- If AKShare is not installed:
pip3 install akshare -q
- If the symbol is wrong or not found, tell the user and suggest the correct format
- For A-share names, you can infer the 6-digit code from common knowledge; if uncertain, say so
- Do NOT show raw JSON or DataFrame output to the user — always parse and format it
