Agent skill · data analytics · 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.
Why this skill is useful
Adds a complete executable workflow for embedding and mining images using Docker, which is not commonly found in public documentation.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
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. Iterative DEFT callers also run the bundled history-aware post-processing step so a sample selected in an earlier iteration is not selected again. The workflow is fixed and deterministic: embed the targets, embed the source pool, mine nearest neighbours, then (for iterative workflows) remove previously mined samples. Each GPU step's output parquet is the next step's input; history-aware selection is a host-side post-processing step, not another k-NN search. There is no clustering pass or human-in-the-loop selection — depth comes from picking the right encoder and a topn wide enough to leave novel candidates after history filtering. The whole skill is a thin wrapper around three direct docker run invocations against the pinned TAO data-services image plus one optional bundled host-Python post-processor for iterative history (see versions.yaml, resolved at runtime in 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-mine-aoi-images