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Found 185 Skills
Comprehensive guide for AntV L7 geospatial visualization library. Use when users need to: (1) Create interactive maps with WebGL rendering (2) Visualize geographic data (points, lines, polygons, heatmaps) (3) Build location-based data dashboards (4) Add map layers, interactions, or animations (5) Process and display GeoJSON, CSV, or other spatial data (6) Integrate maps with AMap (GaodeMap), Mapbox, Maplibre, or standalone L7 Map (7) Optimize performance for large-scale geographic datasets
Fetch, store, and visualize GitHub repository traffic data (views, clones, referrers, stars) with trend charts. Requires repo push access.
Assemble multi-panel scientific figures with panel labels (A, B, C) at publication quality (300 DPI) using R. Use when combining individual plots into journal-ready figures.
Visualize relationships between two variables. Use for correlation analysis and pattern identification.
Visualize competitive positioning using sector charts. Use for market analysis and competitive strategy.
Analyze Walmart sales data to explore trends between store sales and unemployment rates. Generate insightful visualizations and a beautiful HTML report with deep analysis. Suitable for quick insights into the relationship between sales data and macroeconomic factors.
This skill should be used when users need to analyze CSV or Excel files, understand data patterns, generate statistical summaries, or create data visualizations. Trigger keywords include "analyze CSV", "analyze Excel", "data analysis", "CSV analysis", "Excel analysis", "data statistics", "generate charts", "data visualization", "分析CSV", "分析Excel", "数据分析", "CSV分析", "Excel分析", "数据统计", "生成图表", "数据可视化".
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that don't fit in memory.
Common style patterns, layer configurations, and recipes for typical mapping scenarios including restaurant finders, real estate, data visualization, navigation, and more. Use when implementing specific map use cases or looking for proven style patterns.
Data visualization with Recharts 3.x including responsive charts, custom tooltips, animations, and accessibility for React applications. Use when building charts or dashboards with Recharts.
Create interactive maps with markers, heatmaps, routes, and choropleth layers. Use when visualizing geographic data, plotting locations, or creating map-based reports.
Generate interactive graph visualizations in the browser from any data - codebases, infrastructure, relationships, knowledge maps