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tao-generate-anomalies

Full PAIDF AnomalyGen pipeline — fine-tune on a new anomaly dataset, generate synthetic anomaly images (SDG), evaluate quality (nn_score), and search per-sample (guidance, crop_ratio) parameters. Three modes: full (Phase 0→7: finetune then generate), finetune_only (Phase 0→1: train only), inference_only (Phase 0, 2→7: generate from an existing checkpoint). Use when the user asks to "fine-tune AnomalyGen", "generate anomaly images", "run PAIDF SDG", "evaluate SDG output quality", "run per-sample search", or run any part of the AnomalyGen pipeline, even if they only mention one phase.

skills/tao-generate-anomalies/SKILL.md

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# TAO Generate Anomalies

> **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).

Multi-phase pipeline (0–7); the `mode` flag selects which phases run.

| Phase | What runs | Mode(s) |
|---|---|---|
| 0 | Verify / download pretrained checkpoints | all |
| 1 | Fine-tune on `dataset_dir` | `full`, `finetune_only` |
| 2 | Prepare inference JSONL (AMP routing) | `full`, `inference_only` |
| 3 | SDG — generate synthetic anomaly images → `original/` | `full`, `inference_only` |
| 4 | Eval `original/` — emit `per_sample.csv` + `eval.log`, merge `nn_score` into `SDG_result.csv` | `full`, `inference_only` |
| 5 | Per-sample `(guidance, crop_ratio)` search rounds → `rounds/round_NN/` (each round runs SDG + eval) | `full`, `inference_only` |