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Get Started Free →产业链Skill - 提供申万行业分类、行业成分股、产业链上下游分析 via AkShare
.claude/skills/aifinlab-akshare-industry/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 65% | 0% |
| case-02 | ✗→✓ | ▲ Improved | 67% | 0% |
| case-03 | ✗→✓ | ▲ Improved | 68% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 38% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 95% | 0% |
| 属性 | 内容 | |:---|:---| | 名称 | akshare-industry | | 版本 | 1.1.0 | | 分类 | 产业链 | | 状态 | ✅ 已上线 | | 维护者 | FinClaw Core Team | | 最后更新 | 2026-03-19 |
产业链Skill，提供申万行业分类、行业成分股查询、产业链上下游分析。支持从宏观产业链角度分析投资机会，识别上下游关联公司。
| 用户输入 | 识别意图 | 调用函数 | |:---|:---|:---| | 半导体行业有哪些股票？ | industry_stocks | industry_stocks.py 半导体 | | 新能源产业链 | industry_chain | industry_chain.py 新能源 | | 申万行业列表 | industry_list | industry_list.py | | 医药板块股票 | industry_stocks | industry_stocks.py 医药生物 |
| 数据类型 | 主要来源 | 备用来源 | 认证要求 | |:---|:---|:---|:---:| | 行业分类 | AkShare-申万 | - | 无需 | | 行业成分股 | AkShare | - | 无需 | | 产业链数据 | AkShare | - | 无需 |
| 代码 | 行业名称 | 典型代表 | |:---:|:---|:---| | 801010 | 农林牧渔 | 牧原股份、温氏股份 | | 801020 | 基础化工 | 万华化学、恒力石化 | | 801030 | 钢铁 | 宝钢股份、鞍钢股份 | | 801040 | 有色金属 | 紫金矿业、洛阳钼业 | | 801050 | 建筑材料 | 海螺水泥、东方雨虹 | | 801060 | 建筑装饰 | 中国建筑、中国中铁 | | 801070 | 电力设备 | 宁德时代、隆基绿能 | | 801080 | 机械设备 | 三一重工、恒立液压 | | 801090 | 国防军工 | 中航沈飞、中国船舶 | | 801100 | 汽车 | 比亚迪、长城汽车 | | 801110 | 家用电器 | 美的集团、格力电器 | | 801120 | 食品饮料 | 贵州茅台、五粮液 | | 801130 | 纺织服饰 | 海澜之家、森马服饰 | | 801140 | 轻工制造 | 欧派家居、太阳纸业 | | 801150 | 医药生物 | 恒瑞医药、迈瑞医疗 | | 801160 | 公用事业 | 长江电力、中国核电 | | 801170 | 交通运输 | 顺丰控股、中远海控 | | 801180 | 房地产 | 保利发展、万科A | | 801200 | 商贸零售 | 中国中免、永辉超市 | | 801210 | 社会服务 | 锦江酒店、宋城演艺 | | 801230 | 综合 | - | | 801710 | 建筑材料 | 同801050 | | 801720 | 建筑装饰 | 同801060 | | 801730 | 电力设备 | 同801070 | | 801740 | 计算机 | 海康威视、科大讯飞 | | 801750 | 传媒 | 分众传媒、芒果超媒 | | 801760 | 通信 | 中兴通讯、中国移动 | | 801770 | 银行 | 招商银行、工商银行 | | 801780 | 非银金融 | 中国平安、中信证券 | | 801790 | 综合金融 | 同花顺、东方财富 | | 801880 | 汽车 | 同801100 | | 801890 | 机械设备 | 同801080 |
bash python scripts/industry_list.py
bash python scripts/industry_stocks.py 半导体 python scripts/industry_stocks.py 801070 # 电力设备
bash python scripts/industry_chain.py 新能源 python scripts/industry_chain.py 半导体
| 脚本名 | 功能 | 入口点 | |:---|:---|:---:| | industry_list.py | 行业列表 | ✅ | | industry_stocks.py | 行业成分股 | ✅ | | industry_chain.py | 产业链分析 | ✅ |
上游: 锂矿/钴矿 → 正极材料 → 负极材料 → 电解液 → 隔膜
↓
中游: 电芯制造 → 电池PACK → 电机 → 电控
↓
下游: 新能源汽车 → 充电桩 → 运营服务上游: 硅片 → 光刻胶 → 电子特气 → CMP材料
↓
中游: 芯片设计 → 晶圆制造 → 封装测试
↓
下游: 消费电子 → 汽车电子 → AI芯片markdown--- 📊 **数据来源**: AkShare-申万行业分类 ⏱️ **数据时间**: 2026-03-19 📌 **分类标准**: 申万一级行业(2021版) 🔗 **原始来源**: 申万宏源研究 🔧 **分析工具**: FinClaw v1.0
akshare>=1.10.0
pandas>=1.3.0
pyyaml>=5.4.0| 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31个申万行业表 | | 1.0.0 | 2026-03-13 | 初始版本 |
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-01 | fail→pass | 16,672 | 29,111 | +75% | 1 | 1 | 0% | 3,547 | 5,842 | +65% | 0 | 0 | — |
case-02 | fail→pass | 19,240 | 18,270 | -5% | 1 | 1 | 0% | 3,196 | 5,331 | +67% | 0 | 0 | — |
case-03 | fail→pass | 22,027 | 23,987 | +9% | 1 | 1 | 0% | 3,450 | 5,811 | +68% | 0 | 0 | — |
case-14 | fail→pass | 13,157 | 5,650 | -57% | 1 | 1 | 0% | 2,096 | 2,886 | +38% | 0 | 0 | — |
case-04 | fail→pass | 6,663 | 4,116 | -38% | 1 | 1 | 0% | 1,311 | 2,562 | +95% | 0 | 0 | — |
case-05 | fail→pass | 28,455 | 5,089 | -82% | 1 | 1 | 0% | 4,531 | 2,619 | -42% | 0 | 0 | — |
case-06 | fail→pass | 7,097 | 4,322 | -39% | 1 | 1 | 0% | 1,171 | 2,420 | +107% | 0 | 0 | — |
case-07 | fail→pass | 18,008 | 14,949 | -17% | 1 | 1 | 0% | 2,742 | 4,204 | +53% | 0 | 0 | — |
case-08 | fail→fail | 14,688 | 25,952 | +77% | 1 | 1 | 0% | 2,612 | 5,430 | +108% | 0 | 0 | — |
case-09 | fail→fail | 19,164 | 24,654 | +29% | 1 | 1 | 0% | 3,487 | 5,979 | +71% | 0 | 0 | — |
case-10 | fail→fail | 19,795 | 6,786 | -66% | 1 | 1 | 0% | 1,530 | 2,918 | +91% | 0 | 0 | — |
case-11 | fail→pass | 12,132 | 2,645 | -78% | 1 | 1 | 0% | 2,038 | 2,200 | +8% | 0 | 0 | — |
case-12 | fail→pass | 15,081 | 3,350 | -78% | 1 | 1 | 0% | 2,497 | 2,267 | -9% | 0 | 0 | — |
case-13 | fail→pass | 12,258 | 3,467 | -72% | 1 | 1 | 0% | 1,989 | 2,443 | +23% | 0 | 0 | — |
case-15 | fail→pass | 12,112 | 2,720 | -78% | 1 | 1 | 0% | 1,952 | 2,327 | +19% | 0 | 0 | — |
case-16 | fail→pass | 7,784 | 2,749 | -65% | 1 | 1 | 0% | 828 | 2,292 | +177% | 0 | 0 | — |
case-17 | fail→pass | 12,065 | 6,614 | -45% | 1 | 1 | 0% | 1,936 | 2,838 | +47% | 0 | 0 | — |
case-18 | pass→pass | 20,366 | 4,057 | -80% | 1 | 1 | 0% | 2,975 | 2,293 | -23% | 0 | 0 | — |
case-19 | fail→pass | 7,183 | 3,663 | -49% | 1 | 1 | 0% | 938 | 2,477 | +164% | 0 | 0 | — |
case-20 | fail→fail | 14,248 | 8,705 | -39% | 1 | 1 | 0% | 2,176 | 3,140 | +44% | 0 | 0 | — |
case-21 | pass→pass | 18,812 | 14,375 | -24% | 1 | 1 | 0% | 2,561 | 3,845 | +50% | 0 | 0 | — |
case-22 | pass→pass | 9,821 | 15,876 | +62% | 1 | 1 | 0% | 1,508 | 4,133 | +174% | 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. 22 cases were attempted. The headline lift of +68 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.