Total 50,523 skills, Data Processing has 2561 skills
Showing 12 of 2561 skills
Manipulation and analysis of planar geometric objects. Based on the widely deployed GEOS library. Provides data structures for points, curves, and surfaces, and standardized algorithms for geometric operations. Use for 2D geometry operations, spatial relationships, set-theoretic operations (intersection, union, difference), point-in-polygon queries, geometric calculations (area, distance, centroid), buffering, simplifying geometries, linear referencing, and cleaning invalid geometries. Essential for GIS operations, spatial analysis, and geometric computations.
Under the assumption that the US dollar or a certain currency loses its reserve status and gold becomes the only anchor, deduce the 'implied gold price that the balance sheet can withstand' by dividing central bank monetary liabilities by gold reserves, and output the leverage level, gap and ranking of each country or currency.
Access Airtable bases, tables, and records. Use when user mentions Airtable, bases, tables, records, or spreadsheet data. Uses Python pyairtable library for clean, reliable access.
Search and extract contact information for people or companies including names, phone numbers, emails, job titles, and LinkedIn profiles. Aggregates data from multiple sources and provides enriched contact details. Use when users need to find contact information, build prospect lists, or enrich existing contact data.
通过ASIN获取亚马逊商品详细信息，包括标题、图片、五点描述、规格参数、A+页面、价格、评分评论、变体等。当用户提到亚马逊商品详情、ASIN查询、商品页面数据、Listing分析、五点描述提取、商品图片获取、变体查看、竞品Listing研究、价格查询、评论拆解、商品规格查询、Amazon product details, ASIN lookup, listing analysis, bullet points, variant info, product pricing, ratings and reviews, A+ content, product specifications, product images时触发此技能。即使用户未明确说"商品详情"，只要其需求涉及通过ASIN获取亚马逊商品页面的结构化数据，也应触发此技能。
Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.
Cross-market financial metrics batch query — revenue, net profit, ROE, debt ratio, free cash flow, gross margin for one or more symbols across HK / US / A-share / SG markets. Supports multi-symbol horizontal comparison, similar to natural-language financial screening. Triggers: "财务数据查询", "财务指标", "营收查询", "净利润查询", "ROE查询", "负债率", "现金流查询", "毛利率查询", "财务数据批量", "財務數據查詢", "財務指標", "營收查詢", "淨利潤查詢", "ROE查詢", "負債率", "現金流查詢", "毛利率查詢", "financial data query", "revenue query", "net profit query", "ROE query", "debt ratio", "free cash flow", "gross margin query", "financial metrics", "financial comparison", "batch financials".
Static basic information for all Longbridge-tradable securities — stocks, ETFs, options, warrants: company name, listing date, exchange, industry classification, total shares, circulating shares, market cap, IPO price, website, address. Futures / bonds / funds have limited coverage. Triggers: "基础信息", "股票信息", "上市日期", "总股本", "流通股", "IPO价格", "标的信息", "品种信息", "基礎信息", "股票資料", "上市日期", "總股本", "流通股", "IPO價格", "基本資料", "basic info", "stock info", "listing date", "shares outstanding", "IPO price", "symbol info", "static data", "security info", "exchange listing", "total shares".
Seasonality and calendar-effect strategy via Longbridge Securities — uses historical OHLCV data to compute month-of-year returns (January Effect), day-of-week returns (Monday / Friday effect), pre/post-holiday drift, and earnings-season effect; identifies statistically significant patterns and generates trading signals. Triggers: "季节性", "日历效应", "月份效应", "周一效应", "年初效应", "节假日效应", "财报季效应", "时间模式", "季節性", "日曆效應", "月份效應", "周一效應", "年初效應", "節假日效應", "財報季效應", "seasonality", "calendar effect", "January effect", "day of week effect", "holiday effect", "earnings season effect", "seasonal pattern", "time series anomaly", "月度效应", "月度效應", "monthly seasonality".
Pairs trading / statistical-arbitrage strategy via Longbridge Securities — tests cointegration between two correlated assets using the Engle-Granger (ADF) method, computes the optimal hedge ratio via OLS, calculates spread Z-score, half-life of mean reversion, and generates entry/exit signals (long spread when Z > 2, short spread when Z < -2, exit when |Z| < 0.5). Triggers: "配对交易", "统计套利", "协整", "价差交易", "对价交易", "双股套利", "配對交易", "統計套利", "協整", "價差交易", "pairs trading", "statistical arbitrage", "cointegration", "spread trading", "mean reversion pairs", "hedge ratio", "half-life", "ADF test", "Kalman filter", "Z-score spread", "spread mean reversion".
Comprehensive market scanner — combines real-time quotes, capital flow (large/medium/small order distribution), and candlestick data for multi-symbol technical analysis (MACD / RSI / Bollinger Bands computed from OHLCV). Supports batch multi-symbol scanning. Triggers: "行情扫描", "综合行情", "多标的扫描", "行情数据", "实时行情综合", "技术+资金综合", "行情指标", "行情監控", "行情掃描", "綜合行情", "多標的掃描", "技術+資金綜合", "market scanner", "comprehensive quote", "multi-stock scan", "real-time data", "market data query", "technical plus capital flow", "market overview", "batch quote", "technical indicators", "MACD RSI scan".
Event-driven investment strategy — identify and analyse corporate events (M&A, spinoffs, buybacks, index rebalancing, lockup expiry) that create pricing dislocations. Framework: event identification → sentiment scoring → historical price reaction → position sizing. Uses Longbridge news / filings / calendar data as signal inputs. Triggers: "事件驱动", "并购套利", "指数调整", "解禁套利", "事件策略", "公司事件策略", "事件投资", "套利机会", "事件驅動", "並購套利", "指數調整", "解禁套利", "事件策略", "公司事件策略", "event-driven", "event strategy", "merger arbitrage", "index rebalancing", "lockup expiry", "event investing", "corporate event trading", "special situation", "spinoff", "buyback catalyst".