Total 50,473 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Expert-level Tableau Desktop/Server, calculated fields, LOD expressions, dashboards, data blending, and performance optimization
Expert-level data mesh architecture, domain-oriented ownership, data products, federated governance, and self-serve platforms
Create Databricks AI/BI dashboards. CRITICAL: You MUST test ALL SQL queries via execute_sql BEFORE deploying. Follow guidelines strictly.
Use when "statistical modeling", "A/B testing", "experiment design", "causal inference", "predictive modeling", or asking about "hypothesis testing", "feature engineering", "data analysis", "pandas", "scikit-learn"
Multi-year financial trend comparison, regression detection, and anomaly flagging for tax planning and audit risk assessment
Use when selecting statistical methods, performing power analysis, guiding uncertainty quantification, or validating MCMC/Monte Carlo implementations.
Headless spreadsheet engine for financial modeling, data analysis, and scenario comparison. Use when: building financial models with ratios and what-if scenarios, computing derived values from tabular data with formulas, producing .xlsx files with live formulas (not static values) for human review, any task where the agent would otherwise write imperative code to manipulate numbers that a spreadsheet does naturally. Triggers: financial model, scenario analysis, ratio computation, balance sheet, P&L, what-if, sensitivity analysis, banking ratios, spreadsheet model, build a model, projection, forecast. Do NOT use for: simple CSV/Excel read/write (use the xlsx skill), chart-only tasks, or data volumes exceeding ~5000 rows.
Coordinate the plot point and dramatic function analysis process, manage text preprocessing, parallel analysis, and result integration. Suitable for scenarios of plot point and dramatic function analysis of long texts, and scenarios requiring structured analysis reports
Integrate multiple plot point analysis results into a comprehensive report, and generate high-quality analysis through deduplication, classification, sorting, and summarization. Suitable for integrating multiple analysis sources and generating unified reports
Fast in-process analytical database for SQL queries on DataFrames, CSV, Parquet, JSON files, and more. Use when user wants to perform SQL analytics on data files or Python DataFrames (pandas, Polars), run complex aggregations, joins, or window functions, or query external data sources without loading into memory. Best for analytical workloads, OLAP queries, and data exploration.
Product analytics expert using PostHog MCP. Triggers on requests to understand user behavior, surface insights, create dashboards, analyze funnels, track metrics, set up experiments, or answer questions about product performance. Use when working with PostHog data, discussing analytics strategy, investigating user journeys, retention, conversion, feature adoption, or when asked to help understand what's happening in the product.
Trade mean reversion setups in the style of Scarface Trades, the mean reversion specialist known for mathematical precision and statistical edge. Emphasizes standard deviation bands, RSI extremes, and calculated entries with defined risk. Use when trading overextended moves, fading extremes, or building systematic reversion strategies.