Total 54,172 skills, Data Processing has 2771 skills
Showing 12 of 2771 skills
DataWorks metadata Skill for Alibaba Cloud — browse Data Map metadata and perform non-destructive writes via Aliyun CLI. READ scope: list/get catalogs, databases, tables, columns, partitions; query data lineage (upstream/downstream impact); list/get datasets & versions; list/get metadata collections (Category/Album) and entities inside them; preview dataset version content. WRITE scope (non-destructive only): update table & column business metadata; register lineage relationships; create/update datasets and versions; create/update metadata collections and add entities to them. This Skill exposes NO delete or remove APIs — every `delete-*` and `remove-*` operation is intentionally out of scope. For deletions, use the DataWorks console. Triggers: "dataworks metadata", "data map", "data lineage", "meta collection", "dataset", "catalog", "table info", "column info", "partition", "impact analysis", "register lineage", "create dataset", "update business metadata".
End-to-end data engineering pipeline using Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
ETL pipeline and analytics application for Harvard Art Museums API with SQL storage and Streamlit visualization
Snowplow Analytics integration. Manage data, records, and automate workflows. Use when the user wants to interact with Snowplow Analytics data.
Builds Moran's I spatial autocorrelation workflows in CARTO. Triggers when the user mentions spatial autocorrelation, Moran's I, spatial dependency, spatial correlation, spatial outliers, HH HL LH LL quadrants, high-high clusters, low-low clusters, spatial weight matrix, "is there clustering", "are values spatially correlated", local indicators of spatial association, LISA, spatial randomness test, or wants to determine whether a variable exhibits spatial clustering, dispersion, or randomness across a gridded dataset. Also relevant when the user needs to classify locations into cluster types (HH, HL, LH, LL) rather than just identifying hotspots and coldspots.
Guides the user through spatial enrichment workflows — triggered by requests to enrich, add demographics, estimate population around locations, compute spatial features, sociodemographic analysis, "what's around" queries, buffer/isochrone + join patterns, or trade area enrichment.
Use this skill when the user wants to manage data quality in DataHub: create or run assertions, check assertion outcomes, raise or resolve incidents, create notification subscriptions, or diagnose health problems across their estate. Triggers on: "create assertion", "run assertion", "check quality", "data quality", "health check", "raise incident", "resolve incident", "subscribe to", "failing assertions", "active incidents", or any request involving data quality, assertions, incidents, or quality notifications.
Load data into MotherDuck from local files, object storage, HTTPS, dataframes, or external databases. Use when choosing a MotherDuck-specific ingestion path, especially CTAS and INSERT...SELECT, bulk loading, secrets, and Postgres-endpoint versus DuckDB-client tradeoffs.
8 finance skills. Trigger: financial modeling, market data, risk analysis, quantitative finance. Design: data sources, quantitative methods, and regulatory frameworks.
16 full-text access skills. Trigger: accessing paper PDFs, bulk downloading, open access, text mining. Design: legal full-text retrieval from open repositories, archives, and preprint servers.
Use when writing QGIS expressions for filtering, labeling, symbology, or field calculations. Prevents expression syntax errors and context misconfiguration. Covers QgsExpression parsing, evaluation contexts, field calculator, data-defined properties, and custom functions. Keywords: QgsExpression, expression, field calculator, label expression, data-defined, @qgsfunction, filter, evaluate, calculate field, formula, conditional label, dynamic value.
Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation, optimization, publication figures, sensor/time-series data, citation lookup, and common scientific libraries. Use when the user asks for MATLAB code, scientific Python, data analysis, plots, simulations, formulas, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.