Total 53,816 skills, Data Processing has 2767 skills
Showing 12 of 2767 skills
Model, forecast, and interpret volatility using time-series models and options-implied measures. Use when the user asks about EWMA, GARCH models, implied volatility, volatility surfaces, volatility term structure, or the VIX. Also trigger when users mention 'volatility smile', 'volatility skew', 'realized vs implied vol', 'volatility risk premium', 'vol clustering', 'mean-reverting volatility', 'options pricing inputs', 'RiskMetrics', 'decay factor', or ask how to forecast future volatility for risk management.
Open, create, read, analyze, edit, or validate Excel/spreadsheet files (.xlsx, .xlsm, .csv, .tsv). Use when the user asks to create, build, modify, analyze, read, validate, or format any Excel spreadsheet, financial model, pivot table, or tabular data file. Covers: creating new xlsx from scratch, reading and analyzing existing files, editing existing xlsx with zero format loss, formula recalculation and validation, and applying professional financial formatting standards. Triggers on 'spreadsheet', 'Excel', '.xlsx', '.csv', 'pivot table', 'financial model', 'formula', or any request to produce tabular data in Excel format.
Parser Expert integration. Manage data, records, and automate workflows. Use when the user wants to interact with Parser Expert data.
Spatial and spatiotemporal regression with GNNWR (Geographically Neural Network Weighted Regression). Use when Claude needs to: (1) Build spatially varying coefficient regression models, (2) Analyze geographic non-stationarity in spatial data, (3) Generate spatial coefficient maps for publication, (4) Run spatiotemporal regression with GTNNWR, (5) Scale geographically weighted regression to large datasets (N > 10k) with KNN mode, (6) Diagnose spatial model performance with F-tests, AIC, and residual maps.
Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
Comprehensive guide for Azure Data Explorer (ADX) and Kusto Query Language (KQL); use when writing/optimizing KQL queries, setting up ingestion, building dashboards, doing time-series/ML analysis, configuring management/security, or when users mention Kusto, KQL, ADX, Azure Data Explorer, or log analytics queries.
Kinetica SQL query knowledge. Activate when the user is writing analytical queries for Kinetica, asking about Kinetica-specific functions, or working with geospatial, time-series, graph, or vector data.
Visualize relationships between two variables. Use for correlation analysis and pattern identification.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Develop Microsoft Fabric Spark/data engineering workflows with intelligent routing to specialized resources. Provides core workspace/lakehouse management and routes to: data engineering patterns, development workflow, or infrastructure orchestration. Use when the user wants to: (1) manage Fabric workspaces and resources, (2) develop notebooks and PySpark applications, (3) design data pipelines and orchestration, (4) provision infrastructure as code. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "infrastructure provisioning", "Delta Lake patterns", "Spark development", "lakehouse configuration", "organize lakehouse tables", "create Livy session", "notebook deployment".
Analyze management's discussion of competitive landscape, market positioning, and strategic differentiation from earnings call transcripts.
Build end-to-end ETL pipelines with Harvard Art Museums API, SQL analytics, and Streamlit visualization