Discover and use data skills to extend Claude's capabilities
665 Data Skills Available
Integrate with Microsoft Excel for spreadsheets. Use when you need to: (1) read and write Excel data, (2) manage spreadsheet workbooks, or (3) automate data analysis workflows.
Expert guide for Layer Cake, a headless Svelte visualization framework managing scales, dimensions, and data flow while supporting SVG, Canvas, HTML, and WebGL rendering contexts for responsive data visualizations.
Create interactive 2D data visualizations using D3.js with zoom, pan, and custom rendering
Analyze student enrollment data with focus on student-centric queries including student counts, enrollment analysis, course loads, and tracking student enrollment changes over time.
Executive-grade data analysis with pandas/polars and McKinsey-quality visualizations. Use when analyzing data, building dashboards, creating investor presentations, or calculating SaaS metrics.
Routes data processing, knowledge graph, and analytics tasks. Triggers on graph, vector, knowledge, ontology, process, batch, etl, database, query, csv, json.
Database schema specification using Mermaid ER diagrams, table structures, constraints, and indexes for multi-tenant applications.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. Use when working with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Compact single-cell analysis patterns with AnnData and Scanpy. Essential patterns for QC, preprocessing, clustering, and visualization.
Create custom layer types with WebGL rendering, custom tile layers, and blend layers. Use for advanced visualizations and custom data sources.
Style and render geographic data with renderers, symbols, and visual variables. Use for creating thematic maps, heatmaps, class breaks, unique values, labels, and 3D visualization.
Work with temporal data using TimeSlider, TimeExtent, and time-aware layers. Use for animating data over time, filtering by date ranges, and visualizing temporal patterns.
Write Arcade expressions for dynamic calculations in popups, renderers, labels, and field calculations. Use for data-driven styling, custom labels, and computed fields.
Master relational and NoSQL databases. Learn PostgreSQL, MySQL, MongoDB, Redis, and other technologies for data persistence, optimization, and scaling.
Expert data science guidance for analytics, data processing, visualization, statistical analysis, machine learning, and AI integration. Use when analyzing data, building ML models, creating visualizations, processing datasets, conducting A/B tests, optimizing metrics, or integrating AI features. Includes Python (pandas, scikit-learn), data pipelines, and model deployment.
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
Master SQL for data analysis with complex queries, joins, aggregations, window functions, and query optimization.
Detect anomalies in metrics and time-series data using OPAL statistical methods. Use when you need to identify unusual patterns, spikes, drops, or outliers in observability data. Covers statistical outlier detection (Z-score, IQR), threshold-based alerts, rate-of-change detection with window functions, and moving average baselines. Choose pattern based on data distribution and anomaly type.
Use OPAL subquery syntax (@labels) and union operations to combine multiple datasets or time periods. Essential for period-over-period comparisons, multi-dataset analysis, and complex data transformations. Covers @label <- @ syntax, timeshift for temporal shifts, union for combining results, and any_not_null() for collapsing grouped data.
[Aesthetics] Extracts recurring visual patterns from references: contrast habits, shapes, density/whitespace balance, rhythm, textures. Produces Aesthetic Pattern Library as Data-Sheet nodes in Brain canvas.
Data analysis, visualization, statistical modeling, and reproducible research workflows.
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.
Evidence framework data dashboard with DuckDB. Write SQL in markdown to create visualizations.
Эксперт по data annotation. Используй для ML labeling, annotation workflows и quality control.
Analyzes content marketing metrics to identify top performers, trends, and optimization opportunities. Use when reviewing blog posts, social media, or campaign performance. Accepts CSV data with engagement metrics and provides actionable insights.
Frontend-backend time data transmission standard, using millisecond timestamps for time points (timezone-independent); Duration/Period use ISO 8601 string format.
Parse and analyze personal financial transaction CSV exports to calculate account totals and generate detailed breakdowns. Use when the user asks to analyze transaction data, generate financial summaries, calculate account balances, or review spending from CSV exports. Supports account grouping (Galicia, Mercado Pago, Quiena, LLC/Relay, HSBC, Crypto), automatic internal transfer detection, and detailed transaction listings.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
R 4.4+ best practices with testthat 3.2, lintr 3.2, and data analysis patterns.
Extracts structured data from cybersecurity fatigue research papers and calculates statistical correlations