arcgis-cim-symbols
Create advanced cartographic symbols using CIM (Cartographic Information Model). Use for complex multi-layer symbols, animated markers, custom line patterns, and data-driven symbology.
Create advanced cartographic symbols using CIM (Cartographic Information Model). Use for complex multi-layer symbols, animated markers, custom line patterns, and data-driven symbology.
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.
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
Shipping Data Analysis - Brock's Reusable Prompt v4.0 Analyzes any shipping dataset with FirstMile-branded deliverables, normalized address grouping, and conditional advanced analytics based on available data fields. Use when: (1) Prospect provides shipping data (PLD, exports, etc.) (2) "analyze shipping data" or "shipping analysis" (3) "run Brock's prompt" or "data analysis v4.0" (4) Need DIM exposure, zone distribution, or cost intelligence reports (5) Creating prospect-facing shipping profile analysis Triggers on: "shipping analysis", "analyze shipping", "PLD analysis", "Brock's prompt", "data analysis v4.0", "shipping profile", "DIM exposure", "zone distribution"
Master data manipulation, analysis, and visualization with Pandas, NumPy, and Matplotlib
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.
Common patterns for extracting and combining analytics data from GA4, GSC, and SE Ranking. Includes API patterns, rate limiting, caching, and error handling.
Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.
This skill should be used when the user asks to "query Chicago data", "find Chicago datasets", "get Chicago crime data", "download Chicago permits", "write a SODA query for Chicago", "search data.cityofchicago.org", or mentions Chicago city data (311, permits, licenses, inspections, crimes, etc.).
Execute code to analyze data and perform complex calculations.
Create custom layer types with WebGL rendering, custom tile layers, and blend layers. Use for advanced visualizations and custom data sources.
Elementos conectados visualmente se perciben como relacionados. Use cuando diseñe diagramas, flujos, relaciones entre elementos, o conexiones visuales.
Use when creating tree diagrams, force-directed networks, Voronoi diagrams, or hierarchical layouts. Invoke for org charts, node-link diagrams, treemaps, dendrograms, force simulations, spatial indexing, or network visualizations.
This skill should be used when the user asks to "create a plot", "make a chart", "visualize data", "create a heatmap", "make a scatter plot", "plot time series", "create publication figures", "customize plot styling", "use matplotlib", "use seaborn", or needs guidance on Python data visualization, statistical graphics, or figure export.
Core data analytics concepts, Excel/Google Sheets fundamentals, and data collection techniques
This skill should be used when the user asks to "query Cook County data", "find Cook County datasets", "get property assessments", "download parcel data", "search datacatalog.cookcountyil.gov", "get medical examiner data", "find court cases", "query State's Attorney data", or mentions Cook County government data (assessor, treasurer, courts, payroll, medical examiner, etc.).
Master data visualization principles including chart selection, dashboard design, color theory, and data storytelling
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.