Total 30,768 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Skill that helps users discover and understand Dagster integration libraries. Used when users have requests related to integrating with other tools / technologies, or when have users have questions related to specific integration libraries (dagster-*).
Use to convert probabilities into decisions (bet/pass/hedge) and optimize scoring. Invoke when need to calculate edge, size bets optimally (Kelly Criterion), extremize aggregated forecasts, or improve Brier scores. Use when user mentions betting strategy, Kelly, edge calculation, Brier score, extremizing, or translating belief into action.
Guide AI agents to generate complete PageObject pattern web scraper projects using Playwright and TypeScript with Docker deployment. Supports agent-browser site analysis for automated selector discovery. Keywords: scraper, playwright, pageobject, web scraping, docker, typescript, data extraction, automation.
Create publication-quality charts and graphs for economics papers.
Comprehensive guide for writing modern Neo4j Cypher read queries. Essential for text2cypher MCP tools and LLMs generating Cypher queries. Covers removed/deprecated syntax, modern replacements, CALL subqueries for reads, COLLECT patterns, sorting best practices, and Quantified Path Patterns (QPP) for efficient graph traversal.
Complete T-SQL function reference for SQL Server and Azure SQL Database. Use this skill when: (1) User asks about T-SQL string, date, math, or conversion functions, (2) User needs help with window/ranking functions, (3) User works with JSON or XML in T-SQL, (4) User asks about aggregate functions or GROUP BY, (5) User needs system or metadata functions.
An analytical in-process SQL database management system. Designed for fast analytical queries (OLAP). Highly interoperable with Python's data ecosystem (Pandas, NumPy, Arrow, Polars). Supports querying files (CSV, Parquet, JSON) directly without an ingestion step. Use for complex SQL queries on Pandas/Polars data, querying large Parquet/CSV files directly, joining data from different sources, analytical pipelines, local datasets too big for Excel, intermediate data storage and feature engineering for ML.
Best practices for NumPy array programming, numerical computing, and performance optimization in Python
Merge multiple CSV/Excel files with intelligent column matching, data deduplication, and conflict resolution. Handles different schemas, formats, and combines data sources. Use when users need to merge spreadsheets, combine data exports, or consolidate multiple files into one.
Retrieves protein structure data from RCSB PDB, PDBe, and AlphaFold with protein disambiguation, quality assessment, and comprehensive structural profiles. Creates detailed structure reports with experimental metadata, ligand information, and download links. Use when users need protein structures, 3D models, crystallography data, or mention PDB IDs (4-character codes like 1ABC) or UniProt accessions.
Gather comprehensive biological target intelligence from 9 parallel research paths covering protein info, structure, interactions, pathways, expression, variants, drug interactions, and literature. Features collision-aware searches, evidence grading (T1-T4), explicit Open Targets coverage, and mandatory completeness auditing. Use when users ask about drug targets, proteins, genes, or need target validation, druggability assessment, or comprehensive target profiling.
Analyze earnings call transcripts to extract key insights about future guidance, strategic priorities, management commentary, and market signals.