${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_platforms.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} --format text

${TAO_SKILL_BANK_PATH:-~/tao-skills-external}/scripts/list_tao_platforms.py \
  --skill-bank ${TAO_SKILL_BANK_PATH:-~/tao-skills-external} \
  --platform <platform> --format text

<workspace>/
├── .env                                     # NGC_API_KEY (nvcr.io/* image pulls), HF_TOKEN (HuggingFace pre-flight pulls). No AnomalyGen credentials required — this EA variant ingests pre-generated pairs.
├── specs/baseline_spec.yaml                 # ChangeNet train/eval spec
├── train/base/
│   ├── training_set.csv                     # seed training rows; ChangeNet 14-column siamese schema
│   └── validation_set.csv                   # held-out rows; checked for leakage against every train CSV
├── kpi/
│   ├── images/                              # KPI test images (real data only — no generated images here)
│   └── testing_set.csv                      # labels live in the CSV
├── augmentation/
│   ├── mining_pool/
│   │   ├── mining_pool.csv                  # append-only production-line samples; paths relative to this dir
│   │   └── images/                          # source images referenced by mining_pool.csv (e.g. *_SolderLight.jpg)
│   └── anomalygen/                          # customer-supplied pre-generated synthetic pairs (this EA variant does not run AnomalyGen)
│       ├── reconstructed_image/             # NG images (will become ChangeNet input_path); flat dir of *.jpg or *.png
│       ├── original_image/                  # OK partner images, same stems as reconstructed_image/ (will become ChangeNet golden_path)
│       └── defect_spec.jsonl                # OPTIONAL — one entry per defect_type if defect-type accounting is wanted in deft_state.json
│                                            # Stems in reconstructed_image/ and original_image/ must match 1-to-1; extensions may differ.
└── results/run_<YYYYMMDD_HHMMSS>/           # created/resumed by this workflow (= ${RESULTS_DIR})

results/run_<YYYYMMDD_HHMMSS>/               # = ${RESULTS_DIR}
├── deft_state.json                          # current resume snapshot (schema: references/deft_state.json)
├── loop_log.jsonl                           # append-only stage log; single source of truth
├── DEFT_Loop_Report.html                    # re-rendered after every stage by agents/reporter.md
├── best_model.json                          # inference handoff metadata (see references/prepare-for-inference.md)
├── best_model_inference_spec.yaml           # ready-to-run TAO inference spec built from training config
├── iter${ITER}_summary.md                   # ≤300-word per-iteration summary
├── synth_pool/                              # built ONCE at Pre-Flight step 10 via scripts/prestage_pregen.py
│   ├── manifest.json                        # paths + counts for the loop to reference
│   ├── images/synth_{ng,ok}/                # ChangeNet-staged pre-gen pairs (single copy, shared across iters)
│   ├── sdg_rows.csv                         # 14-col + provenance + filepath; the SDG half of source_pool
│   ├── source_pool.{csv,parquet}            # real (mining_pool) + sdg unified pool with provenance
│   ├── source_embeddings.parquet            # written only when --embed-with-siglip was passed to prestage_pregen.py
│   └── source_embed.log                     # data-services log for the source embedding (if run)
├── baseline/
│   ├── train/                               # TAO train output: model_epoch_<EEE>_step_<SSS>.pth × N, status.json, experiment.yaml, train.log
│   ├── inference/{best_val,latest}/         # per-checkpoint inference.csv + KPI plots from scripts/analyze_kpi.py
│   └── rca_results/<TS>/                    # kpi_gaps.parquet, threshold.txt, weak_samples_breakdown.txt
└── iter${ITER}/
    ├── routing_results/<TS>/                # mining_gaps.parquet, anomalygen_gaps.parquet, routing_summary.txt
    ├── anomalygen/                          # per-iter bookkeeping (just records the synth_pool/manifest.json path)
    │   └── ingest_summary.json              # per-iter audit: which synth_pool manifest was reused, counts at iter start
    ├── mining_filter/
    │   ├── mining_pool.csv                  # top-K-per-target k-NN survivors from synth_pool/source_pool (synth + real subject to same filter)
    │   ├── knn_summary.csv                  # candidate_count, kept_count, rejected_count, similarity_threshold=0.9
    │   ├── target_embeddings.parquet        # embeddings of weak-target images (per-iter — targets change each iter)
    │   └── mining_summary.txt               # per-label breakdown emitted by mining container
    ├── dataset/
    │   ├── train_combined_iter${ITER}.csv
    │   └── train_combined_iter${ITER}_provenance.csv  # source ∈ {base_train, previous_iter_train, mining_pool}
    ├── train/                               # TAO train output for iter${ITER}
    ├── inference/{best_val,latest}/
    └── rca_results/<TS>/                    # next iteration's RCA reads inference/{best_val|latest}/inference.csv