Generate GIS-based city guide maps from location names or POI data.
npx skills add https://github.com/Hatari130/map-creator --skill map-creator
Please help me install this Agent Skill. Skill: map-creator Source: https://github.com/Hatari130/map-creator/tree/main Command: npx skills add https://github.com/Hatari130/map-creator --skill map-creator
Creating high-quality city guide maps is a complex, multi-step process involving POI retrieval, coordinate system conversion, GIS rendering, and visual styling. This Skill automates this entire pipeline, allowing users to generate professional-grade, reviewable maps from simple lists of locations.
Use the map-creator skill to generate a city guide map for the city of Nanjing with the locations SLAB TOWN and Village Bar titled Nanjing Coffee Guide.
High-intent search queries and answers about installing and using this skill.
To generate a city guide map from a list of locations, you input location names or POI data to automate OpenStreetMap retrieval, coordinate transformation, and GIS rendering into a professional cartographic output. The workflow handles road networks, buildings, and water bodies automatically.
The best way to convert OpenStreetMap data into a stylized map poster is using an automated GIS workflow that retrieves spatial data, aligns coordinates, and applies AI-driven visual styling. This transforms technical GIS drafts into high-end, hand-drawn style posters suitable for print.
Yes, the automated GIS workflow handles GCJ-02 to WGS84 coordinate transformations to ensure spatial accuracy. It automatically aligns coordinates during the OpenStreetMap data retrieval and rendering pipeline without requiring manual projection setup.
Yes, you can use GeoPandas and OSMnx for automated cartographic text placement. The workflow leverages these dependencies to retrieve spatial data, manage geometric operations with Shapely, and render accurate text placement using Matplotlib.
To create a professional walking map for a travel blog, input your POI data like a list of coffee shops. The workflow retrieves OpenStreetMap data, renders the GIS draft with road networks, and applies stylized aesthetics to produce an accurate neighborhood guide.
Limitations of using Python scripts for automated GIS rendering include dependency on external libraries like GeoPandas, OSMnx, and Matplotlib. Complex spatial queries or highly customized cartographic aesthetics may require manual adjustments beyond the automated OpenStreetMap data retrieval pipeline.
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