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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.

skills/tao-mine-aoi-images/SKILL.md

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# 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 image_embeddings -e <embedding_spec.yaml> …` for embedding and `tmm nearest_neighbors -e <mining_spec.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 "$DS_IMAGE"` (after resolving `$DS_IMAGE` per Setup).

Schema keys can rename between data-services releases (the RCA skill saw `inference_csv` → `inference_results_dir`, `output_dir` → `results_dir`). When in doubt, introspect the actual schema once per image: `docker run --rm "$DS_IMAGE" embedding image_embeddings --cfg=job` and `... tmm nearest_neighbors --cfg=job`.

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