Agent skill · NVIDIA
tao-mine-aoi-images
Runs the DEFT embed-then-mine workflow for VCN AOI iterations — embeds the gap-analysis target parquet, embeds a source pool, and mines nearest-neighbour source images for downstream augmentation. Use as the immediate next step after `tao-route-visual-changenet-samples` when expanding a real-image augmentation queue from the mining subset.
What it needs
About 7k tokens when loaded.
What this skill does
DEFT Mining and Embedding Skill Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery). You are the operator of the DEFT embed-then-mine workflow for VCN AOI. Your job is to take a parquet of weak target images (the gap-analysis or routing output) and a source pool, then produce a deduplicated parquet of mined source images that look similar to the targets — ready to feed into the next training round. The workflow is fixed and deterministic: embed the targets, embed the source pool, then mine nearest neighbours. Each step's output parquet is the next step's input. There is no iterative search, no clustering pass, no human-in-the-loop selection — depth comes from picking the right encoder and the right topn, not from a multi-phase investigation. The whole skill is a thin wrapper around three direct docker run invocations against the pinned TAO data-services image (see versions.yaml (resolved at runtime — see Setup). The container's entrypoint takes <category> <action> -e <spec.yaml> [hydra overrides...] — pass embedding imageembeddings -e <embeddingspec.yaml> … for embedding and tmm nearestneighbors -e <miningspec.yaml> … for mining. The -e flag points at a YAML that supplies default values for the subtask's schema; anything afterward is a bare Hydra override (key=value) that selectively overrides spec fields per run. (There is no dataset keyword inside the container — that's the TAO launcher's pillar prefix and is dropped here.) Pull the image once if it isn't cached: docker pull "$DSIMAGE" (after resolving $DSIMAGE per Setup). Schema keys can rename between data-services releases (the RCA skill saw inferencecsv → inferenceresultsdir, outputdir → resultsdir). When in doubt, introspect the actual schema once per image: docker run --rm "$DSIMAGE" embedding imageembeddings --cfg=job and ... tmm nearestneighbors --cfg=job. --- Inputs 1. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tao-mine-aoi-images