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.

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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

.

# HK stock
python3 /Users/henry/.agentara/.claude/skills/stock/fetch_stock.py hk 9988

# A-share
python3 /Users/henry/.agentara/.claude/skills/stock/fetch_stock.py cn 600519

# US stock
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):

1. Latest row = most recent trading day data
2. Latest price =

   close

   of last row
3. Latest change% =

   pct

   of last row
4. Avg volatility = mean of

   |pct|

   of rows 2–21 (excluding last row)
5. Avg volume = mean of

   vol

   of rows 2–6 (5-day avg, excluding last)
6. 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