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Get Started Free →A股股票数据Skill - 提供实时行情、历史数据、板块分析、资金流向等 via AkShare/腾讯财经
.claude/skills/aifinlab-akshare-stock/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-07 | ✗→✓ | ▲ Improved | 22% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 43% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-10 | ✗→✓ | ▲ Improved | 55% | 0% |
| case-11 | ✗→✓ | ▲ Improved | 103% | 0% |
| 属性 | 内容 | |:---|:---| | 名称 | akshare-stock | | 版本 | 1.1.0 | | 分类 | 股票数据 | | 状态 | ✅ 已上线 | | 维护者 | FinClaw Core Team | | 最后更新 | 2026-03-19 |
A股股票数据Skill，提供实时行情、历史数据、板块分析、资金流向、龙虎榜等全方位A股数据服务。优先使用腾讯财经API（稳定、快速），备用AkShare（东方财富数据源）。
| 用户输入 | 识别意图 | 调用函数 | |:---|:---|:---| | 茅台今天多少钱？ | stock_realtime | stock_quote_tx.py 600519 | | 查一下宁德时代股价 | stock_realtime | stock_quote_tx.py 300750 | | 半导体板块有哪些股票 | sector_analysis | stock_sector.py 半导体 | | 今天资金流向如何 | capital_flow | stock_capital.py | | 查询贵州茅台历史数据 | stock_history | stock_hist.py 600519 |
| 数据类型 | 主要来源 | 备用来源 | 认证要求 | |:---|:---|:---|:---:| | 实时行情 | 腾讯财经 | 新浪财经 | 无需 | | 历史K线 | AkShare-东方财富 | Baostock | 无需 | | 板块数据 | AkShare-东方财富 | - | 无需 | | 资金流向 | AkShare-东方财富 | - | 无需 | | 龙虎榜 | AkShare-东方财富 | - | 无需 |
bash python scripts/stock_quote_tx.py 600519
bash python scripts/stock_search_tx.py 茅台
bash python scripts/stock_sector.py 半导体
bash python scripts/stock_capital.py --market sh --limit 30
bash python scripts/stock_lhb.py python scripts/stock_lhb.py 20250311
bash python scripts/stock_hist.py 600519 20260101 20260319
| 脚本名 | 功能 | 入口点 | |:---|:---|:---:| | stock_quote_tx.py | 腾讯财经实时行情 | ✅ | | stock_search_tx.py | 股票搜索（腾讯） | ✅ | | stock_list.py | 股票列表筛选 | ✅ | | stock_sector.py | 板块/行业分析 | ✅ | | stock_capital.py | 资金流向分析 | ✅ | | stock_lhb.py | 龙虎榜数据 | ✅ | | stock_hist.py | 历史K线数据 | ✅ | | stock_quote.py | 实时行情（AkShare备用） | ❌ | | stock_search.py | 股票搜索（AkShare备用） | ❌ |
bash# 实时行情 python scripts/stock_quote_tx.py 600519 python scripts/stock_quote_tx.py 000001 # 股票搜索 python scripts/stock_search_tx.py 茅台 python scripts/stock_search_tx.py 宁德时代 # 股票列表 python scripts/stock_list.py --market sh --limit 20 # 板块分析 python scripts/stock_sector.py 半导体 python scripts/stock_sector.py 新能源 # 资金流向 python scripts/stock_capital.py --limit 20 # 龙虎榜 python scripts/stock_lhb.py
pythonfrom finclaw.core.data_fetcher import fetch_stock_realtime from finclaw.core.data_annotator import format_stock_quote # 获取实时行情 result = fetch_stock_realtime("600519") if result.success: data = result.data # 格式化输出（自动带数据来源标注） output = format_stock_quote( stock_code=data['code'], stock_name=data['name'], price=data['price'], change=data['change'], change_pct=data['change_pct'], source=result.source ) print(output)
本Skill所有输出数据将按以下格式标注来源：
markdown--- 📊 **数据来源**: 腾讯财经 / AkShare-东方财富 ⏱️ **数据时间**: 2026-03-19 10:30:15 ⚡ **获取延迟**: 234ms 🔗 **数据接口**: https://qt.gtimg.cn 🔧 **分析工具**: FinClaw v1.0
akshare>=1.10.0
pandas>=1.3.0
requests>=2.25.0
pyyaml>=5.4.0| 变量名 | 说明 | 必需 | |:---|:---|:---:| | FINCLAW_DATA_SOURCE_CONFIG | 数据源配置文件路径 | 否 |
| 错误码 | 说明 | 处理建议 | |:---|:---|:---| | NETWORK_ERROR | 网络连接失败 | 检查网络，切换备用数据源 | | INVALID_CODE | 股票代码无效 | 确认代码格式（6位数字） | | DATA_NOT_FOUND | 数据不存在 | 检查是否为交易日/交易时间 | | RATE_LIMIT | 请求频率过高 | 降低请求频率，添加延时 |
| 指标 | 目标值 | 当前值 | |:---|:---:|:---:| | 实时行情延迟 | < 500ms | ~200ms | | 可用性 | > 99% | 99.5% | | 数据准确率 | > 98% | 99% |
| 市场 | 代码格式 | 示例 | |:---|:---|:---| | 上海证券交易所 | 6xxxxx | 600519 (贵州茅台) | | 深圳证券交易所 | 0xxxxx | 000001 (平安银行) | | 创业板 | 3xxxxx | 300750 (宁德时代) | | 科创板 | 688xxx | 688981 (中芯国际) | | 北交所 | 8xxxxx/4xxxxx | 835185 (贝特瑞) |
半导体、银行、白酒、新能源、医药、房地产、汽车、人工智能、芯片、5G、光伏、锂电池
| 版本 | 日期 | 变更内容 | |:---|:---:|:---| | 1.1.0 | 2026-03-19 | 符合FinClaw数据规范v1.0，新增数据来源强制标注 | | 1.0.0 | 2026-03-12 | 初始版本 |
finclaw/config/data_source_config.yaml本Skill遵循 FinClaw 数据规范 v1.0 | 数据来源强制标注 | 禁止训练数据编造
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-22 | pass→pass | 16,766 | 4,517 | -73% | 1 | 1 | 0% | 2,783 | 3,283 | +18% | 0 | 0 | — |
case-01 | fail→fail | 13,497 | 9,308 | -31% | 1 | 1 | 0% | 1,947 | 3,829 | +97% | 0 | 0 | — |
case-02 | fail→fail | 18,991 | 17,959 | -5% | 1 | 1 | 0% | 2,748 | 5,959 | +117% | 0 | 0 | — |
case-03 | fail→fail | 12,687 | 11,387 | -10% | 1 | 1 | 0% | 2,332 | 3,487 | +50% | 0 | 0 | — |
case-04 | fail→fail | 28,940 | 26,970 | -7% | 1 | 1 | 0% | 5,322 | 6,885 | +29% | 0 | 0 | — |
case-05 | fail→fail | 26,529 | 29,741 | +12% | 1 | 1 | 0% | 4,378 | 7,560 | +73% | 0 | 0 | — |
case-06 | pass→pass | 16,080 | 10,693 | -34% | 1 | 1 | 0% | 2,156 | 4,224 | +96% | 0 | 0 | — |
case-07 | fail→pass | 15,876 | 7,068 | -55% | 1 | 1 | 0% | 2,858 | 3,486 | +22% | 0 | 0 | — |
case-08 | fail→pass | 15,282 | 5,831 | -62% | 1 | 1 | 0% | 2,481 | 3,536 | +43% | 0 | 0 | — |
case-09 | fail→pass | 16,791 | 4,300 | -74% | 1 | 1 | 0% | 2,358 | 3,240 | +37% | 0 | 0 | — |
case-10 | fail→pass | 14,756 | 9,404 | -36% | 1 | 1 | 0% | 2,487 | 3,848 | +55% | 0 | 0 | — |
case-11 | fail→pass | 12,366 | 3,870 | -69% | 1 | 1 | 0% | 1,650 | 3,347 | +103% | 0 | 0 | — |
case-12 | fail→pass | 15,541 | 3,665 | -76% | 1 | 1 | 0% | 1,985 | 3,279 | +65% | 0 | 0 | — |
case-13 | pass→pass | 15,123 | 6,825 | -55% | 1 | 1 | 0% | 2,241 | 3,693 | +65% | 0 | 0 | — |
case-14 | fail→pass | 18,929 | 5,460 | -71% | 1 | 1 | 0% | 3,604 | 3,315 | -8% | 0 | 0 | — |
case-15 | fail→pass | 13,142 | 3,377 | -74% | 1 | 1 | 0% | 2,454 | 2,973 | +21% | 0 | 0 | — |
case-16 | fail→pass | 10,565 | 3,179 | -70% | 1 | 1 | 0% | 1,455 | 3,027 | +108% | 0 | 0 | — |
case-17 | fail→pass | 15,249 | 5,514 | -64% | 1 | 1 | 0% | 2,087 | 3,476 | +67% | 0 | 0 | — |
case-18 | pass→pass | 10,641 | 7,151 | -33% | 1 | 1 | 0% | 1,487 | 3,816 | +157% | 0 | 0 | — |
case-19 | pass→pass | 6,228 | 6,078 | -2% | 1 | 1 | 0% | 878 | 3,295 | +275% | 0 | 0 | — |
case-20 | fail→pass | 11,551 | 3,023 | -74% | 1 | 1 | 0% | 1,917 | 3,042 | +59% | 0 | 0 | — |
case-21 | fail→pass | 9,373 | 5,409 | -42% | 1 | 1 | 0% | 1,644 | 3,540 | +115% | 0 | 0 | — |
case-23 | fail→pass | 10,216 | 2,840 | -72% | 1 | 1 | 0% | 1,895 | 3,008 | +59% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 23 cases were attempted, and 22 counted toward the lift figure. The other 1 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +57 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.