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name: foundation-models-embeddings-vlms description: "Route GeoAI foundation models, embeddings, DINOv3, Prithvi, UniverSat, TESSERA, Moondream, vLLM, and caption workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Foundation models, embeddings, and VLMs

Use this sub-skill when the task centers on GeoAI foundation-model registry lookup, precomputed embeddings, DINOv3 similarity, Prithvi, UniverSat, TESSERA, AlphaEarth/Google satellite embeddings, Moondream, vLLM geospatial VLMs, or BLIP/spaCy captioning.

Route here

• Foundation model discovery, registry filtering, TerraTorch-backed model loading decisions, or model-name normalization.
• Existing embedding dataset analysis, clustering, similarity search, lightweight classifiers, or embedding GeoTIFF export.
• DINOv3 patch similarity, feature-map visualization, and DINOv3 workflow selection.
• Prithvi, UniverSat, TESSERA, AlphaEarth, Moondream, vLLM, and captioning API choice and preflight.

Route away

• Generic training, broad fine-tuning, dataset layout validation, or DINOv3 segmentation training details: route to training-and-finetuning.
• Segmentation/object-detection inference products and vectorized mask outputs: route to detection-segmentation-inference.
• STAC search, NAIP/Overture downloads, raster tiling, CRS repair, or pipeline config prep: route to geospatial-data-pipelines.

Start points

• Model and embedding workflows
• API reference
• Troubleshooting
• No-download registry reporter

Prefer metadata, optional-dependency probes, and existing embeddings first. Only load model weights, call Hugging Face, download embedding tiles, start a vLLM service, or run long inference when the user explicitly accepts those runtime costs.