Total 52,363 skills, Data Processing has 2634 skills
Showing 12 of 2634 skills
Query real-time macroeconomic data including GDP, unemployment, inflation (CPI, PCE), interest rates, housing data, and consumer sentiment. Use this skill whenever the user asks about macroeconomic conditions, economic indicators, the Fed, interest rates, inflation, employment, labor market, housing market, yield curve, or any broad economic data point.
基于ByteHouse MCP Server，生成数据资产目录和血缘分析的技能，用于获取数据库表结构、生成数据资产目录、分析表之间的血缘关系。当用户需要获取ByteHouse数据库的表结构、生成数据资产目录、分析表之间的血缘关系时，使用此Skill。
Fetch structured stock sentiment across Reddit, X.com, news, and Polymarket using the Adanos Finance API. Use this skill whenever the user asks how much people are talking about a stock, how hot a ticker is on social platforms, how many Polymarket bets exist for a company, whether sources are aligned, or to compare stock sentiment across multiple tickers. Triggers include: "social sentiment on TSLA", "how hot is NVDA on X.com", "how many Reddit mentions does AAPL have", "compare sentiment on AMD vs NVDA", "how many Polymarket bets on Microsoft", "is Reddit aligned with X on META", "stock buzz", "bullish percentage", and any mention of cross-source stock sentiment research. This skill is READ-ONLY and does not place trades or modify anything.
Terminus App integration. Manage data, records, and automate workflows. Use when the user wants to interact with Terminus App data.
Factor research framework for evaluating single-factor effectiveness across A-shares, HK, and US stocks — information coefficient (IC), information ratio (IR), decile portfolio backtests, and IC decay (serial autocorrelation). Triggers: "因子研究", "IC分析", "信息比率", "分层回测", "因子有效性", "单因子测试", "因子衰减", "因子评估", "IC分析", "信息比率", "分層回測", "因子有效性", "單因子測試", "factor research", "information coefficient", "IC", "IR information ratio", "factor backtest", "decile portfolio", "factor decay", "factor effectiveness".
Multi-factor cross-sectional stock-selection strategy via Longbridge Securities — scores stocks in an index or candidate pool on value (1/PE, 1/PB), momentum (60-day return), quality (ROE), and low-volatility (60-day HV) factors; standardises to Z-scores; composites with equal or IC-weighted combination; constructs a TopN long portfolio (high-score group) and bottom-N short portfolio. Triggers: "多因子", "因子选股", "量化选股", "多因子模型", "因子投资", "横截面", "TopN组合", "IC权重", "多因子", "因子選股", "量化選股", "多因子模型", "橫截面", "multi-factor", "factor investing", "quantitative stock selection", "cross-sectional factor", "factor model", "IC weighting", "factor composite", "TopN portfolio", "factor score", "Z-score ranking".
Behavioral finance application framework — identify cognitive biases in markets (overreaction, underreaction, disposition effect, anchoring, herding), translate them into quantifiable trading signals (momentum / reversal), and assess whether current market sentiment shows systematic bias. Triggers: "行为金融", "认知偏差", "过度反应", "反应不足", "处置效应", "锚定效应", "羊群效应", "市场情绪偏差", "行為金融", "認知偏差", "過度反應", "反應不足", "處置效應", "錨定效應", "羊群效應", "behavioral finance", "cognitive bias", "overreaction", "underreaction", "disposition effect", "anchoring bias", "herding", "sentiment bias", "behavioral economics".
Sector screening and ranking — filter and rank A-share / HK / US industry sectors by valuation (PE/PB), capital inflow, price performance (1d/5d/20d), and turnover rate. Outputs a sector leaderboard. Triggers: "板块筛选", "行业筛选", "强势板块", "弱势板块", "板块排行", "行业排名", "资金流入板块", "涨幅最大板块", "板塊篩選", "行業篩選", "強勢板塊", "弱勢板塊", "板塊排行", "行業排名", "sector screener", "sector filter", "sector ranking", "top sectors", "hot sectors", "capital inflow sectors", "sector scan", "industry ranking", "sector performance", "best sectors today".
Aggregates PayPal disputes, HubSpot feedback and tickets, and email sentiment (plus pasted or exported Google/Yelp reviews) into a themes report with verbatim evidence and a "do these three things this week" list. Use when the user asks how customers are feeling, for review analysis, what people are saying, or about disputes.
Use when designing or modifying Elasticsearch ingest pipelines, including single-path parsing, branching logic, sub-pipelines, enrichment processors, and robust on_failure handling.
Extract, validate, and categorize invoice data against purchase orders and GL codes
Build interactive financial KPI dashboards with customizable metrics, drill-down analysis, variance explanations, and automated threshold-based alerting