Total 50,483 skills, Data Processing has 2559 skills
Showing 12 of 2559 skills
Power BI report visualization design prompt for creating effective, user-friendly, and accessible reports with optimal chart selection and layout design.
Validates JSON data against JSON Schema using the z-schema library. Use when the user needs to validate JSON, check data against a schema, handle validation errors, use custom format validators, work with JSON Schema drafts 04 through 2020-12, set up z-schema in a project, compile schemas with cross-references, resolve remote $ref, configure validation options, or inspect error details. Covers sync/async modes, safe error handling, schema pre-compilation, remote references, TypeScript types, and browser/UMD usage.
Databricks documentation reference. Use as a lookup resource alongside other skills and MCP tools for comprehensive guidance.
Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
Databricks development guidance including Python SDK, Databricks Connect, CLI, and REST API. Use when working with databricks-sdk, databricks-connect, or Databricks APIs.
Expert guidance for HTML/XML parsing using BeautifulSoup in Python with best practices for DOM navigation, data extraction, and efficient scraping workflows.
Sync retirement account data from Vanguard and Fidelity CSV exports to Google Sheets DataHub. Handles multiple accounts, aggregates holdings by ticker, and updates quantities in retirement section (rows 46-62). Triggers on sync retirement, update retirement, vanguard sync, 401k update, IRA sync, or working with notebooks/retirement-accounts/ files.
Write and debug spreadsheet formulas (Excel/Google Sheets), pivot tables, and array formulas; translate between dialects; use when users need working formulas with examples and edge-case checks.
Compare document similarity using TF-IDF, cosine similarity, and Jaccard index. Use for plagiarism detection, duplicate finding, or content matching.
Auto-generate features with encodings, scaling, polynomial features, and interaction terms for ML pipelines.
Statistical scoring with z-scores, percentiles, freshness decay, and cross-category normalization. Rank and compare items with confidence scoring.
Install ADBC (Arrow Database Connectivity) drivers with dbc. Use when the user wants to install database drivers and connect to databases.