Total 50,510 skills, Data Processing has 2560 skills
Showing 12 of 2560 skills
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".
Portfolio performance attribution via Longbridge Securities — Brinson industry attribution (allocation / selection / interaction effects), factor alpha/beta decomposition (market β, value, momentum, size), and timing ability (Treynor-Mazuy model). For portfolio review and fund analysis. Requires login with Trade scope. Triggers: "业绩归因", "归因分析", "Brinson归因", "配置效应", "选股效应", "因子归因", "alpha来源", "择时效应", "業績歸因", "歸因分析", "Brinson歸因", "配置效應", "選股效應", "因子歸因", "performance attribution", "Brinson attribution", "allocation effect", "selection effect", "factor attribution", "alpha decomposition", "timing ability", "portfolio attribution", "T-M model", "Jensen alpha".
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".
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".
Salesforce Data Cloud Retrieve phase. Use this skill when the user runs Data Cloud SQL, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use querying-soql), segment creation or calculated insight design (use segmenting-datacloud), or STDM/session tracing/parquet analysis (use observing-agentforce).
Salesforce Data Cloud Harmonize phase. Use this skill when the user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. TRIGGER when: user works with DMOs, mappings, relationships, identity resolution, unified profiles, data graphs, or universal IDs. DO NOT TRIGGER when: the task is only about streams/DLOs (use preparing-datacloud), segments/insights (use segmenting-datacloud), retrieval/search (use retrieving-datacloud), or STDM/session tracing (use observing-agentforce).
Salesforce Data Cloud Prepare phase. Use this skill when the user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations. TRIGGER when: user creates or manages Data Cloud data streams, DLOs, transforms, or Document AI configurations, or asks about ingestion into Data Cloud. DO NOT TRIGGER when: the task is connection setup only (use connecting-datacloud), DMOs and identity resolution (use harmonizing-datacloud), or query/search work (use retrieving-datacloud).
Best practices for handling Evernote data. Use when implementing data storage, processing notes, handling attachments, or ensuring data integrity. Trigger with phrases like "evernote data", "handle evernote notes", "evernote storage", "process evernote content".
Parse, convert, geocode, visualize, and measure geographic data. Use for address cleanup, geo file conversion, mapping, and distance workflows.