Total 50,402 skills, Data Processing has 2557 skills
Showing 12 of 2557 skills
SQLiteData queries, @Table models, Point-Free SQLite, RETURNING clause, FTS5 full-text search, CloudKit sync, CTEs, JSON aggregation, @DatabaseFunction
Fast Python framework for building interactive web apps, dashboards, and data visualizations without HTML/CSS/JavaScript. Use when user wants to create data apps, ML demos, dashboards, data exploration tools, or interactive visualizations. Transforms Python scripts into web apps in minutes with automatic UI updates.
Analytics engineering for reliable metrics and BI readiness. Build transformation layers, dimensional models, semantic metrics, data quality tests, and documentation. Use when you need dbt or SQL transformation strategy, metrics definition, or analytics data modeling.
Design ETL/ELT pipelines with proper orchestration, error handling, and monitoring. Use when building data pipelines, designing data workflows, or implementing data transformations.
Implement data quality checks, validation rules, and monitoring. Use when ensuring data quality, validating data pipelines, or implementing data governance.
Use when investigating why something happened and need to distinguish correlation from causation, identify root causes vs symptoms, test competing hypotheses, control for confounding variables, or design experiments to validate causal claims. Invoke when debugging systems, analyzing failures, researching health outcomes, evaluating policy impacts, or when user mentions root cause, causal chain, confounding, spurious correlation, or asks "why did this really happen?"
Identify stocks where market sentiment is significantly more negative than fundamentals warrant — the gap between narrative and reality. Use when the user asks to find contrarian opportunities, stocks with sentiment-fundamental misalignment, oversold but fundamentally strong companies, stocks punished by negative narratives, or wants to analyze whether market fear is justified for specific stocks or sectors.
Screen and analyze stocks through an ESG (Environmental, Social, Governance) lens, evaluating sustainability practices, controversy exposure, and responsible investing criteria. Use when the user asks about ESG investing, sustainable investing, socially responsible investing (SRI), impact investing, green stocks, carbon footprint analysis, governance quality assessment, controversy screening, exclusion lists, or ESG scoring of companies or portfolios.
Calculate the deviation of asset prices relative to the long-term exponential growth trend line, assess whether the current period falls within a historical extreme range, and optionally perform macro factor analysis to evaluate the market regime.
Creates dbt models following project conventions. Use when working with dbt models for: (1) Creating new models (any layer - discovers project's naming conventions first) (2) Task mentions "create", "build", "add", "write", "new", or "implement" with model, table, or SQL (3) Modifying existing model logic, columns, joins, or transformations (4) Implementing a model from schema.yml specs or expected output requirements Discovers project conventions before writing. Runs dbt build (not just compile) to verify.
Optimizes Snowflake SQL query performance from provided query text. Use when optimizing Snowflake SQL for: (1) User provides or pastes a SQL query and asks to optimize, tune, or improve it (2) Task mentions "slow query", "make faster", "improve performance", "optimize SQL", or "query tuning" (3) Reviewing SQL for performance anti-patterns (function on filter column, implicit joins, etc.) (4) User asks why a query is slow or how to speed it up
Structured data extraction from web pages using claude-in-chrome MCP with sequential-thinking planning. Focus on READ operations, data transformation, and pagination handling for multi-page extraction.