ths-advanced-analysis
基于 thsdk 进行高级股票分析:分钟K线(1m/5m/15m/30m/60m/120m)、板块/指数行情(主要指数/申万行业/概念板块成分股)、多股票批量对比(表格+归一化走势图+相关性热力图)、盘口深度、大单流向、集合竞价异动、日内分时、历史分时。当用户提到"分钟K线"、"日内走势"、"盘口"、"大单"、"竞价异动"、"板块行情"、"行业排名"、"概念板块"、"成分股"、"对比多只股票"、"批量分析"、"涨幅对比"、"相关性"、"港股"、"美股"、"外汇"、"期货"、"资讯"、"快讯",或者需要同时查看2只以上股票、关注短线交易、量化研究时,必须使用此skill。
Best use case
ths-advanced-analysis is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
基于 thsdk 进行高级股票分析:分钟K线(1m/5m/15m/30m/60m/120m)、板块/指数行情(主要指数/申万行业/概念板块成分股)、多股票批量对比(表格+归一化走势图+相关性热力图)、盘口深度、大单流向、集合竞价异动、日内分时、历史分时。当用户提到"分钟K线"、"日内走势"、"盘口"、"大单"、"竞价异动"、"板块行情"、"行业排名"、"概念板块"、"成分股"、"对比多只股票"、"批量分析"、"涨幅对比"、"相关性"、"港股"、"美股"、"外汇"、"期货"、"资讯"、"快讯",或者需要同时查看2只以上股票、关注短线交易、量化研究时,必须使用此skill。
Teams using ths-advanced-analysis should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/ths-advanced-analysis/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How ths-advanced-analysis Compares
| Feature / Agent | ths-advanced-analysis | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
基于 thsdk 进行高级股票分析:分钟K线(1m/5m/15m/30m/60m/120m)、板块/指数行情(主要指数/申万行业/概念板块成分股)、多股票批量对比(表格+归一化走势图+相关性热力图)、盘口深度、大单流向、集合竞价异动、日内分时、历史分时。当用户提到"分钟K线"、"日内走势"、"盘口"、"大单"、"竞价异动"、"板块行情"、"行业排名"、"概念板块"、"成分股"、"对比多只股票"、"批量分析"、"涨幅对比"、"相关性"、"港股"、"美股"、"外汇"、"期货"、"资讯"、"快讯",或者需要同时查看2只以上股票、关注短线交易、量化研究时,必须使用此skill。
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for ChatGPT
Find the best AI skills to adapt into ChatGPT workflows for research, writing, summarization, planning, and repeatable assistant tasks.
AI Agent for Product Research
Browse AI agent skills for product research, competitive analysis, customer discovery, and structured product decision support.
AI Agent for SaaS Idea Validation
Use AI agent skills for SaaS idea validation, market research, customer discovery, competitor analysis, and documenting startup hypotheses.
SKILL.md Source
# THS Advanced Analysis Skill
## 对话引导规范
### 澄清意图(意图模糊时必问)
| 用户说 | 可能的意图 | 必问 |
|--------|-----------|------|
| "帮我看看XX股票" | 实时行情?K线?大单? | ✅ |
| "分析一下XX" | 技术面?资金面?和谁对比? | ✅ |
| "XX板块怎么样" | 整体涨跌?成分股?领涨股? | ✅ |
| "选一些好股票" | 短线?价值?哪个行业?条件? | ✅ |
| "XX的5分钟K线" | 意图明确 | ❌ 直接执行 |
| "今日涨停股" | 意图明确 | ❌ 直接执行 |
**话术示例:**
```
用户:"帮我分析一下宁德时代"
Claude:"好的,请问你主要想看哪个方向?
1. 今日实时行情 + 资金流向
2. 分钟K线(盘中走势)
3. 近期日K线趋势
4. 和比亚迪、亿纬锂能等对比
5. 用问财筛选相关概念股"
```
### 调用后的后续提示(有延伸价值时才提)
| 场景 | 提示 |
|------|------|
| 展示行业排名 | "需要查某个行业的成分股行情吗?" |
| 展示分钟K线 | "需要同时看大单流向或盘口深度吗?" |
| 展示多股对比表格 | "需要展示归一化走势图或相关性吗?" |
| 问财选出候选股 | "需要对这些股票做K线技术验证吗?" |
| 展示竞价异动 | "需要对某只异动股拉盘前分时看细节吗?" |
---
## 完整调用案例
| 文件 | 场景 |
|------|------|
| `examples/01_minute_kline.py` | 分钟K线 + 均线 + 成交量异动标注 |
| `examples/02_sector_industry.py` | 行业排名 + 概念板块成分股 + 指数行情 |
| `examples/03_multi_stock_compare.py` | 多股批量对比:表格 + 归一化走势 + 相关性 |
| `examples/04_bigorder_auction.py` | 大单流向 + 竞价异动扫描 + 分时/盘口 + 资讯 |
| `examples/05_wencai_nlp.py` | 问财NLP:选股/行情/财务/技术/复杂组合 |
---
## 场景速查
| 用户需求 | 方法 |
|---------|------|
| 今日涨停/连板/竞价强势股 | `wencai_nlp("今日涨停,非ST")` |
| 财务选股(ROE/PE/PB) | `wencai_nlp("连续3年ROE大于15%,非ST")` |
| 技术形态选股 | `wencai_nlp("均线多头排列,MACD金叉")` |
| 分钟K线 | `klines(code, interval="5m", count=78)` |
| 今日分时 | `intraday_data(code)` |
| 历史某日分时 | `min_snapshot(code, date="20250101")` |
| 五档盘口 | `depth(code)` |
| 买方/卖方深度详情 | `order_book_bid(code)` / `order_book_ask(code)` |
| 大单流向 | `big_order_flow(code)` |
| 竞价异动扫描 | `call_auction_anomaly("USHA")` |
| 申万行业列表 | `ths_industry()` |
| 概念板块列表 | `ths_concept()` |
| 板块行情(涨幅/市值) | `market_data_block(link_code)` |
| 板块成分股 | `block_constituents(link_code)` |
| 指数行情 | `market_data_index(ths_code)` |
| 多股票对比 | 批量 `market_data_cn` + `klines` |
| 港股行情 | `market_data_hk(code)` |
| 美股行情 | `market_data_us(code)` |
| 外汇汇率 | `market_data_forex(code)` |
| 期货行情 | `market_data_future(code)` |
| 实时资讯/快讯 | `news()` |
| 权息资料 | `corporate_action(code)` |
| 今日IPO / 待申购 | `ipo_today()` / `ipo_wait()` |
---
## 安装
```bash
pip install --upgrade thsdk
```
> 包来源:[PyPI](https://pypi.org/project/thsdk/)
> 系统要求:Python 3.9+,支持 Linux(x86_64/arm64)、macOS(Intel/Apple Silicon)、Windows
---
## 连接
```python
from thsdk import THS
with THS() as ths: # 游客模式,无需账户配置
...
```
---
## 第一步:代码解析
所有中文名/缩写/短代码先用 `search_symbols` 获得完整 THSCODE:
```python
with THS() as ths:
resp = ths.search_symbols("同花顺")
# data → [
# {'THSCODE': 'USZA300033', 'Name': '同花顺',
# 'MarketStr': 'USZA', 'Code': '300033', 'MarketDisplay': '深A'},
# {'THSCODE': 'URFI883404', 'Name': '同花顺情绪指数', 'MarketDisplay': '同指'},
# ]
```
**选码规则:**
| 情况 | 处理 |
|------|-----|
| 0条 | 告知未找到 |
| 1条 | 直接使用 |
| 多条,只有1只A股(MarketDisplay含"沪A"或"深A") | 自动选A股 |
| 多条,多只A股 | 展示列表,等用户选择 |
**市场前缀说明:**
| 前缀 | 含义 |
|------|------|
| `USHA` | 上海A股 |
| `USZA` | 深圳A股 |
| `USHI` | 上海指数 |
| `USZI` | 深圳指数 |
| `USTM` | 北交所 |
| `UHKG` | 港股 |
| `URFI` | 行业/概念板块 |
| `UFXB` | 外汇(基本汇率) |
**常用指数 THSCODE(直接使用,无需 search_symbols):**
| 指数 | THSCODE |
|------|---------|
| 上证指数 | `USHI000001` |
| 深证成指 | `USZI399001` |
| 创业板指 | `USZI399006` |
| 科创50 | `USHI000688` |
| 沪深300 | `USHI000300` |
| 中证500 | `USHI000905` |
| 上证50 | `USHI000016` |
> ⚠️ 指数前缀是 `USHI`/`USZI`,需调用 `market_data_index`,不能用 `market_data_cn`
---
## K线数据
**interval 参数:** `"1m"` / `"5m"` / `"15m"` / `"30m"` / `"60m"` / `"120m"` / `"day"` / `"week"` / `"month"` / `"quarter"` / `"year"`
> ⚠️ 必须写 `"5m"`,不能写 `"5min"`
**count 与 start/end 二选一,不可混用:**
```python
from datetime import datetime
from zoneinfo import ZoneInfo
tz = ZoneInfo('Asia/Shanghai')
with THS() as ths:
# 按条数
resp = ths.klines("USZA300033", interval="5m", count=78)
# 按时间范围
resp = ths.klines("USZA300033", interval="day",
start_time=datetime(2025, 1, 1, tzinfo=tz),
end_time=datetime(2025, 3, 1, tzinfo=tz))
# 前复权(量化回测用)
resp = ths.klines("USHA600519", interval="day", count=250, adjust="forward")
df = resp.df
# 返回字段:时间, 收盘价, 成交量, 总金额, 开盘价, 最高价, 最低价
# 分钟K线"时间"自动转为 datetime;日K"时间"为 datetime(YYYYMMDD)
```
---
## 分时与盘口数据
### 日内分时(当日)
```python
with THS() as ths:
resp = ths.intraday_data("USZA300033")
df = resp.df
# 字段:时间(datetime+tz), 价格, 成交量, 总金额, 领先指标
```
### 历史分时(近一年)
```python
with THS() as ths:
resp = ths.min_snapshot("USZA300033", date="20240315")
df = resp.df
# 字段:时间(timestamp), 价格, 成交量, 外盘成交量, 内盘成交量, 总金额
```
### 五档盘口
```python
with THS() as ths:
resp = ths.depth("USZA300033") # 单只
resp = ths.depth(["USZA300033", "USHA600519"]) # 多只
df = resp.df
# 字段:买1~5价/量, 卖1~5价/量, 代码, 昨收价
```
### 买卖深度详情
```python
with THS() as ths:
resp = ths.order_book_bid("USZA300033") # 买方深度
resp = ths.order_book_ask("USZA300033") # 卖方深度
df = resp.df
```
### 3秒 Tick
```python
with THS() as ths:
resp = ths.tick_level1("USZA300033")
df = resp.df
# 字段:时间(timestamp), 价格, 成交方向, 交易笔数, 当前量
```
### 超级盘口(含委托档位)
```python
with THS() as ths:
resp = ths.tick_super_level1("USZA300033") # 实时
resp = ths.tick_super_level1("USZA300033", date="20240315") # 历史
df = resp.df
# ⚠️ 部分字段值为 4294967295 表示无效数据,需过滤
```
---
## 大单与竞价
### 大单流向
```python
with THS() as ths:
resp = ths.big_order_flow("USZA300033")
df = resp.df
# 字段:时间, 成交方向, 成交量, 总金额, 委托买入价, 委托卖出价
```
### 集合竞价异动(9:15~9:25)
```python
with THS() as ths:
resp_sh = ths.call_auction_anomaly("USHA") # 沪市
resp_sz = ths.call_auction_anomaly("USZA") # 深市
df = resp_sh.df
# 字段:时间, 价格, 总金额, 代码, 名称, 异动类型1(已映射中文)
# 异动类型:涨停试盘/竞价抢筹/大幅高开/急速上涨/大买单试盘 等13种
```
### 早盘集合竞价快照
```python
with THS() as ths:
resp = ths.call_auction("USZA300033")
df = resp.df
# 字段:时间, 成交方向, 成交量, 总金额, 委托买入价, 委托卖出价
```
---
## 板块与指数
### 行业/概念板块列表
```python
with THS() as ths:
resp = ths.ths_industry() # 同花顺行业,约90个
resp = ths.ths_concept() # 概念板块,约390个
df = resp.df
# ⚠️ 仅返回:代码(URFIXXXXXX), 名称
# 涨幅/行情需另调 market_data_block(link_code)
# extra['total_count'] = 板块总数
```
### 板块行情(两步走)
```python
with THS() as ths:
# Step 1:获取板块列表
resp = ths.ths_industry()
target = next(r for r in resp.data if '半导体' in r['名称'])
link_code = target['代码'] # 格式 URFIXXXXXX
# Step 2:查板块行情
resp = ths.market_data_block(link_code, "基础数据")
df = resp.df
# 字段:价格, 涨幅, 成交量, 板块总市值, 板块流通市值,
# 上涨家数, 下跌家数, 领涨股
# query_key 也支持 "扩展"(含板块涨速、主力净流入等)
```
### 板块成分股
```python
with THS() as ths:
resp = ths.block_constituents("URFI883404")
df = resp.df
# 字段:代码(完整THSCODE), 名称
# extra['total_count'] = 成分股总数
```
### 指数列表 & 行情
```python
with THS() as ths:
# 全部指数列表(约580个)
resp = ths.index_list()
df = resp.df # 字段:代码, 名称
# 指数行情(单只或同市场批量)
resp = ths.market_data_index("USHI000001")
resp = ths.market_data_index(["USHI000001", "USHI000300", "USHI000905"])
df = resp.df
# 字段:价格, 涨幅, 涨跌, 成交量, 总金额, 最高价, 最低价
# query_key 也支持 "扩展"(含量比、振幅等)
```
---
## A股行情(market_data_cn)
```python
with THS() as ths:
resp = ths.market_data_cn("USZA300033", "基础数据")
# 也支持列表(同市场)
resp = ths.market_data_cn(["USZA300033", "USZA000001"], "汇总")
df = resp.df
# 基础数据字段:价格, 成交方向, 成交量, 交易笔数, 总金额,
# 涨速, 当前量, 代码, 名称, 昨收价, 开盘价, 最高价, 最低价
```
**query_key 选项:**
| query_key | 含义 |
|-----------|------|
| `"基础数据"` | 价格、涨跌幅、成交量、金额、开高低、涨速 |
| `"基础数据2"` | 精简版基础数据 |
| `"基础数据3"` | 极简(价格、昨收、成交量) |
| `"扩展1"` | 涨幅、涨跌、换手率、量比、主力净流入、委比 |
| `"扩展2"` | 涨幅、换手率、总市值、流通市值、委比 |
| `"汇总"` | 全量字段,多股对比首选 |
> ⚠️ 同市场限制:USHA 和 USZA 不能在同一次调用中混合
---
## 多市场行情
### 港股
```python
with THS() as ths:
# 港股列表
resp = ths.stock_hk_lists()
# 港股行情
resp = ths.market_data_hk("UHKG00700", "基础数据")
df = resp.df
# query_key 支持:"基础数据" / "每股净资产" / "净利润" / "财务指标"
```
### 美股 / 纳斯达克
```python
with THS() as ths:
resp = ths.stock_us_lists() # 美股列表
resp = ths.nasdaq_lists() # 纳斯达克列表
resp = ths.market_data_us("UNQQAAPL", "基础数据")
df = resp.df
# query_key 支持:"基础数据" / "每股净资产" / "每股收益" / "净利润" / "财务指标"
```
### 外汇
```python
with THS() as ths:
resp = ths.forex_list() # 外汇列表(约25个,UFXB前缀)
resp = ths.market_data_forex("UFXBGBPUSD", "基础数据")
df = resp.df
# 字段:价格, 委托买入价, 委托卖出价, 代码, 名称, 昨收价, 开盘价, 最高价, 最低价
# query_key 也支持 "扩展"
```
### 期货
```python
with THS() as ths:
resp = ths.futures_lists() # 主力合约列表
resp = ths.market_data_future("UCFSAU2506", "基础数据")
df = resp.df
# query_key 支持:"基础数据" / "日增仓" / "扩展"
```
### 债券 / ETF
```python
with THS() as ths:
resp = ths.bond_lists() # 可转债列表
resp = ths.fund_etf_lists() # ETF基金列表
resp = ths.fund_etf_t0_lists() # ETF T+0基金列表
resp = ths.market_data_bond("USHD123456", "基础数据")
resp = ths.market_data_fund("USHA510300", "基础数据")
```
---
## 多股票批量对比
```python
import pandas as pd
from collections import defaultdict
from thsdk import THS
stock_names = ["贵州茅台", "五粮液", "泸州老窖"]
with THS() as ths:
# Step 1: 批量解析代码
stock_codes = []
for name in stock_names:
resp = ths.search_symbols(name)
a_shares = [s for s in resp.data
if any(m in s.get('MarketDisplay', '') for m in ['沪A', '深A'])]
if a_shares:
stock_codes.append({'name': name, 'code': a_shares[0]['THSCODE']})
# Step 2: 按市场分组
by_market = defaultdict(list)
for s in stock_codes:
by_market[s['code'][:4]].append(s)
# Step 3: 批量行情
rows = []
for market, stocks in by_market.items():
codes = [s['code'] for s in stocks]
resp = ths.market_data_cn(codes, "汇总")
for i, row in enumerate(resp.data):
row['股票名称'] = stocks[i]['name']
rows.append(row)
quote_df = pd.DataFrame(rows)
# Step 4: 批量K线
klines_data = {}
for s in stock_codes:
resp = ths.klines(s['code'], interval="day", count=30, adjust="forward")
klines_data[s['name']] = resp.df
# Step 5: 归一化走势(起点=100)
for name, df in klines_data.items():
df['归一化'] = df['收盘价'] / df['收盘价'].iloc[0] * 100
# Step 6: 相关性矩阵
returns = pd.DataFrame({name: df['收盘价'].pct_change() for name, df in klines_data.items()})
corr_matrix = returns.corr()
```
**输出规范:**
1. 表格:股票 / 最新价 / 涨幅% / 成交额 / 换手率 / 量比 / 主力净流入 / 总市值
2. 归一化走势折线图(多线,颜色区分)
3. 相关性热力图(量化场景)
---
## 问财自然语言查询(wencai_nlp)
对接 iwencai.com 同一接口,多条件用逗号/分号分隔。
```python
with THS() as ths:
resp = ths.wencai_nlp("连续3日主力净流入,换手率大于5%,非ST")
df = resp.df
# ⚠️ 股票代码格式为 "605366.SH",需转换
```
**返回代码转换:**
```python
def to_ths_code(code_str: str) -> str:
# wencai 返回:'300033.SZ' / '600519.SH' / '835975.BJ'
try:
code, market = str(code_str).split('.')
mapping = {'SH': 'USHA', 'SZ': 'USZA', 'BJ': 'USTM'}
prefix = mapping.get(market.upper(), '')
return f"{prefix}{code}" if prefix else None
except Exception:
return None
df['ths_code'] = df['股票代码'].apply(to_ths_code)
```
**六大查询类型:**
**① 行情 & 盘面**
```python
"今日涨停,非ST"
"连续2日涨停,非一字板,非ST"
"今日涨停原因类别,涨停封单额,封单量"
"竞价涨幅大于3%,竞价量大于昨日成交量5%,非ST"
"主力净流入由大到小排名前20,非ST"
"近10日区间主力资金流向大于5000万,市值大于100亿"
```
**② 板块 & 行业**
```python
"今日申万行业涨跌幅排名"
"今日概念板块涨幅排名前20"
"人工智能概念股,今日涨跌幅,成交额,主力净流入"
"今日涨幅最大的5个概念板块,涨幅,成分股数量"
```
**③ 财务指标**
```python
"连续3年ROE大于15%,非ST,上市大于3年"
"市盈率小于15,股息率大于3%,市净率小于2,非ST"
"市净率小于1,非ST,流通市值大于20亿"
"连续5年分红,股息率大于4%,资产负债率小于60%"
```
**④ 技术形态**
```python
"均线多头排列,MACD金叉,换手率大于3%,非ST"
"均线粘合,平台突破,成交量大于5日均量1.5倍"
"仙人指路,非ST,非停牌"
"250日新高,非ST,沪深A,上市超过250天"
```
**⑤ 复杂组合**
```python
# 短线强势
"均线多头排列,MACD金叉,DIFF上穿中轴,换手率大于1%且小于10%,30日内有2个交易日涨幅大于4%,非ST"
# 竞价打板
"昨日非一字板涨停,今日竞价涨幅大于等于0%且小于等于9.9%,今日隔夜买单额小于10亿,非ST,非科创板"
```
**⑥ 信息查询**
```python
"涨停原因归类前20"
"今日龙虎榜"
"今日大宗交易"
"近一周北向资金净买入前20"
```
| 方法 | 用途 |
|------|------|
| `wencai_nlp(condition)` | **主要用法**。完整自然语言,返回股票列表+字段 |
| `wencai_base(condition)` | 简单字段查询,如 `"所属行业"` |
---
## 实时资讯(news)
```python
import re
with THS() as ths:
resp = ths.news() # 默认:上证指数相关快讯
# 指定个股资讯:
# resp = ths.news(text_id=0x3814, code="300033", market="USZA")
for item in resp.data:
props = dict(re.findall(r'(\w+)=([^\n]+)', item.get('Properties', '')))
print(f"[{props.get('source','')}] {item['Title']}")
print(f" {props.get('summ','')}\n")
# 字段:Op, Time(timestamp), Title, ID, Code, Stock, Properties
# Properties 含:ctime, summ(摘要), source(来源)
```
---
## 其他实用 API
### 权息资料
```python
with THS() as ths:
resp = ths.corporate_action("USZA300033")
df = resp.df
# 字段:时间(int YYYYMMDD), 权息资料(文字)
# 示例:20100312 → "2010-03-12(每十股 转增10.00股 红利3.00元)$"
```
### IPO 数据
```python
with THS() as ths:
resp = ths.ipo_today() # 今日申购/上市
resp = ths.ipo_wait() # 待申购
df = resp.df
# 字段:stock_name, stock_code, order_date, issue_price,
# issue_pe_static, success_rate, order_limit_up,
# industry_name, exchange 等
```
### 市场列表
```python
with THS() as ths:
resp = ths.stock_cn_lists() # 全部A股(约5198只)
resp = ths.stock_hk_lists() # 港股
resp = ths.stock_us_lists() # 美股
resp = ths.stock_bj_lists() # 北交所
resp = ths.nasdaq_lists() # 纳斯达克
resp = ths.stock_b_lists() # B股
resp = ths.bond_lists() # 可转债
resp = ths.fund_etf_lists() # ETF基金
resp = ths.fund_etf_t0_lists() # ETF T+0基金
resp = ths.futures_lists() # 期货主力合约
resp = ths.forex_list() # 外汇(约25个)
df = resp.df # 字段:代码(完整THSCODE), 名称
```
### 代码补全工具
```python
with THS() as ths:
resp = ths.complete_ths_code("USZA300033")
# 用于验证/补全代码信息
```
---
## 错误处理
```python
with THS() as ths:
resp = ths.klines("USZA300033", interval="5m", count=60)
if not resp:
print(f"调用失败: {resp.error}")
elif resp.df.empty:
print("数据为空,可能是非交易时间")
else:
df = resp.df
```
**常见报错:**
| 错误 | 原因 | 解决 |
|------|------|------|
| `"未登录"` | 未 connect | 确保用 `with THS() as ths` |
| `"证券代码必须为10个字符"` | 格式错误 | 先过 `search_symbols` |
| `"一次性查询多支股票必须市场代码相同"` | 沪深混合 | 按市场分组查询 |
| `"无效的周期类型: 5min"` | interval 写法错 | 改为 `"5m"` |
| `"'count' 参数不能与 'start_time' 同时使用"` | 参数冲突 | 二选一 |
**注意事项:**
- 游客账户在部分专业数据/实时数据上可能有权限限制
- 批量拉取时建议加 `time.sleep(0.5)` 避免限流
- `THS` 为同步阻塞,在 FastAPI/asyncio 中需放入线程池
---
## 与 ths-financial-data 的分工
| 场景 | skill |
|------|-------|
| 单只A股行情/资金流向/日K | `ths-financial-data` |
| 分钟K线 / 盘中监控 | **本 skill** |
| 盘口深度 / 大单 / 竞价异动 | **本 skill** |
| 板块/指数行情及成分股 | **本 skill** |
| 多股票批量对比 | **本 skill** |
| 港股/美股/外汇/期货行情 | **本 skill** |
| 实时资讯快讯 | **本 skill** |
| 问财自然语言查询 | 两者均可 |Related Skills
Margin Analysis & Profit Optimization
Analyze gross, operating, and net margins by product line, customer segment, and channel. Identify margin erosion patterns and build pricing power.
Investment Analysis & Portfolio Management Engine
Complete investment analysis, portfolio construction, risk management, and trade execution methodology. Works across stocks, crypto, ETFs, bonds, and alternatives. Zero dependencies — pure agent skill.
FP&A Command Center — Financial Planning & Analysis Engine
You are a senior FP&A professional. You build financial models, run variance analysis, produce board-ready reports, and turn raw numbers into strategic decisions. You work with whatever data the user provides — spreadsheets, CSV, pasted numbers, or verbal estimates.
data-analysis-partner
智能数据分析 Skill,输入 CSV/Excel 文件和分析需求,输出带交互式 ECharts 图表的 HTML 自包含分析报告
onchain-contract-token-analysis
Analyze smart contracts, token mechanics, permissions, fee flows, upgradeability, market risks, and likely attack surfaces for onchain projects. Use when reviewing ERC-20s, launchpads, vaults, staking systems, LP fee routing, ownership controls, proxy setups, or suspicious token behavior.
resume-analysis
简历分析 skill。用于诊断整份简历的完整性、清晰度、岗位相关性、成果表达和结构质量。当用户说“分析简历”“看看我的简历”“简历诊断”时使用。
contradiction-analysis
触发:当问题复杂、存在多个冲突因素、优先级不清,或你不知道应该先解决什么时调用;常见信号包括 trade-off、瓶颈、根因不明、主次不清、多个问题互相牵制。 English: Trigger when a problem contains competing forces, unclear priorities, or no obvious entry point. Use this skill to identify contradictions, isolate the principal contradiction, classify its nature, and choose the right response.
survey-analysis
AI-powered survey response analysis. Analyzes open-ended survey responses, clusters themes, detects sentiment, and generates actionable insights. Uses BERTopic + GPT-4o-mini.
AdvancedMLClassificationSkill
自动化生成工业级机器学习分类算法代码、调用算法做预测、输出准确率对比和可视化结果,支持新手友好的结果解读。
ad-creative-analysis
Analyze ad creatives (images and videos) extracted from competitor research. Use when given a directory of ad images, video files, or transcripts to evaluate ad quality, score visual and messaging effectiveness, assign a scale score for viral/engagement potential, and generate a cross-creative pattern summary. Triggered by requests like "analyze these ads", "score these creatives", "what hooks are competitors using", "evaluate the ad library", "give me a scale score", "analyze the ad folder", or "what's working in these ads".
Amazon Listing Optimizer — Free Listing Analysis & Keyword Research
**Free alternative to Helium 10 ($97/mo) and Jungle Scout ($49/mo).**
swotpal-swot-analysis
Professional SWOT analysis and competitive comparison powered by SWOTPal.com