Total 50,473 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Apply Partial Least Squares SEM (PLS-SEM) with reflective and formative measurement models to maximize explained variance in endogenous constructs. Use this skill when the user has small samples, formative indicators, or exploratory models, needs to assess AVE/CR/HTMT, or when they ask 'should I use PLS or CB-SEM', 'how do I handle formative constructs', or 'what is the path coefficient significance'.
Implement TF-IDF scoring to measure term importance relative to a document corpus. Use this skill when the user needs to rank documents by keyword relevance, extract important terms from text, or build a basic search relevance engine — even if they say 'find relevant documents', 'keyword extraction', or 'term importance'.
Explains what blockchain intelligence is, standard tool categories (explorers, dashboards, tracers, visualizers), and traditional vs crypto payment rails context. Use when the user asks what blockchain intelligence means, how to read on-chain data at a high level, SWIFT vs settlement, stablecoin rails, or which classes of tools exist for chain analysis.
Read, edit, analyze, and create Microsoft Excel files (.xlsx, .xls, .xlsm, .csv, .tsv). Use when a user asks to: (1) Open/read/inspect an Excel file, (2) Edit or modify spreadsheet data, formulas, or formatting, (3) Analyze spreadsheet data and provide insights, statistics, or trends, (4) Create new Excel files with data, formulas, charts, or formatting, (5) Convert between CSV/TSV and Excel formats, (6) Build financial models or dashboards in Excel.
When the user wants to optimize hotel room inventory, manage rate strategies, or improve revenue management. Also use when the user mentions "hotel revenue management," "room allocation," "yield management," "dynamic pricing," "overbooking optimization," "distribution channel management," or "hotel capacity planning." For tour operations, see tour-operations. For hospitality procurement, see hospitality-procurement.
Snowflake integration. Manage data, records, and automate workflows. Use when the user wants to interact with Snowflake data.
Real-time Hong Kong stock market query. Batch obtain real-time quotes of HKEX securities. Triggered when users ask about "Tencent real-time price", "Hong Kong stock real-time market", "HK quote", "HKEX current price".
Scrape lokasi dari Google Maps — coffee shop, restoran, tempat wisata. Trigger: 'scrape google maps', 'cari coffee shop', 'cari tempat makan'
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
Neo4j Python Driver v6 — driver lifecycle, execute_query, managed and explicit transactions, async (AsyncGraphDatabase), result handling, data type mapping, error handling, UNWIND batching, connection pool tuning, and causal consistency. Use when writing Python code that connects to Neo4j via GraphDatabase.driver, execute_query, execute_read, execute_write, AsyncGraphDatabase, neo4j.Result, or RoutingControl. Package name is `neo4j` (not neo4j-driver) since v6. Python >=3.10 required. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT cover driver upgrades or breaking changes — use neo4j-migration-skill. Does NOT cover GraphRAG pipelines (neo4j-graphrag package) — use neo4j-graphrag-skill.
Chan Theory Pattern Recognition — Automatically detect top/bottom fractals, Bi (upward/downward Bi), Segments, Zhongshu, and generate Buy 1/Buy 2/Buy 3/Sell 1/Sell 2/Sell 3 signals. Depends on the czsc library. Triggers: "缠论", "分型", "笔", "中枢", "线段", "一买", "二买", "三买", "一卖", "二卖", "三卖", "缠中说禅", "缠师", "纏論", "分型", "筆", "中樞", "線段", "一買", "二買", "三買", "一賣", "二賣", "三賣", "chanlun", "chan theory", "bi", "zhongshu", "buy point", "sell point", "fractal top bottom", "Chan theory".
Corporate event-driven analysis via Longbridge Securities — captures and classifies events that create pricing dislocations: major-shareholder increases/decreases, equity incentives, private placements, rights issues, buybacks, M&A/restructuring, index rebalancing, and management changes. Combines filings, corporate actions, and shareholder data to produce event signals. Triggers: "公司事件", "事件驱动", "大股东增持", "大股东减持", "股权激励", "定增", "配股", "回购", "并购重组", "指数调整", "管理层变更", "公告分析", "公司事件", "事件驅動", "大股東增持", "大股東減持", "股權激勵", "定增", "配股", "回購", "並購重組", "指數調整", "corporate event", "event-driven", "share buyback", "equity incentive", "major shareholder increase", "rights issue", "M&A arbitrage", "index rebalancing", "insider buying", "corporate action analysis".