Compute and visualize spatial hex-bin heatmaps from RF capture data.
npx skills add https://github.com/kindofluke/albatross-data --skill hex-heatmap
Please help me install this Agent Skill. Skill: hex-heatmap Source: https://github.com/kindofluke/albatross-data/tree/main/.vibe/skills/hex-heatmap Command: npx skills add https://github.com/kindofluke/albatross-data --skill hex-heatmap
Hex heatmap generation for RF capture data is slow; this Skill provides an end-to-end pipeline to compute hex grid heatmaps in PostGIS, buffer results as GeoJSON, and render them on a MapLibre frontend with a polling-based workflow.
Queue a hex heatmap for your dataset and monitor the frontend until the heatmap is ready.
High-intent search queries and answers about installing and using this skill.
To visualize RF signal strength as a hexbin heatmap, you can queue the spatial data for asynchronous PostGIS hex grid aggregation and render the results interactively using a MapLibre frontend with a continuous color ramp.
Computing large geospatial hex grids without blocking the frontend is handled by an asynchronous background workflow that queues the PostGIS aggregation and uses a polling API to check status until the heatmap is ready.
Yes, MapLibre can render PMTiles for asynchronous heatmap processing, as the pipeline stores aggregated GeoJSON results and serves them via PMTiles for dynamic frontend rendering with continuous color ramps.
PostGIS is required to calculate convex hull perimeters and execute the spatial hex grid queries needed to aggregate large geographic RF datasets into manageable heatmap bins.
Hex heatmap generation takes time on large geographic datasets because spatial hex-bin aggregation is computationally intensive, which is why an asynchronous polling-based workflow is used to deliver results.
The pipeline supports batch analytics for large sensor deployments by computing spatial hex-bin heatmaps to identify coverage gaps and visualize signal strength distributions across geographic areas.
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