Individual skill
tao-run-automl-deft-pipeline
Run the canonical NVIDIA AOI three-phase training pipeline — Phase 1 AutoML baseline (HPO), Phase 2 DEFT loop (RCA → SDG → mining → plain-train retrain), Phase 3 AutoML refinement on the DEFT-augmented dataset. Use when the user asks to "run the AOI workflow", "fine-tune my PCB AOI model end-to-end", "improve my AOI ChangeNet model", or "AOI workflow with AutoML" request — route here instead of tao-run-deft-aoi directly unless the user explicitly asks for the DEFT loop ONLY (e.g. "run JUST the DEFT loop", "skip AutoML, only DEFT"). Also handles the same three-phase pattern for non-AOI DEFT applications — AutoML baseline then DEFT loop warm-started from AutoML's winning HPs then post-DEFT AutoML refinement on the iteration-augmented dataset. Trigger phrases include "run the AOI workflow", "AOI end-to-end", "AutoML + DEFT", "AutoML then DEFT", "tune hyperparameters then DEFT", "DEFT with AutoML at both ends", "warm-start DEFT", "improve my AOI model".
skills/tao-run-automl-deft-pipeline/SKILL.md
Source commit fce3cca4170be75e82c64920654d56ee2811b005
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- Observed behavior
- Network or broad shell
- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 3,911
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
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- tao Declared skill tag
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BENCHMARK.mddoc · 7,215 bytesSKILL.mdcore · 17,494 bytesSKILL.mdcore · 17,494 bytesconfig/skillspector-baseline.yamlasset · 834 bytesevals/evals.jsonasset · 819 bytesreferences/consolidated-preflight.mddoc · 6,691 bytesreferences/phase-handoffs.mddoc · 8,929 bytesreferences/pitfalls-and-quality-checks.mddoc · 2,586 bytesreferences/quick-start-example.mddoc · 2,910 bytesskill-card.mddoc · 4,821 bytesskill.oms.sigasset · 5,849 bytes
Read excerpt
# AutoML + DEFT Pipeline
A workflow-bridge skill that runs **three phases** in sequence by delegating to two existing skills — `tao-run-automl` for HPO and a DEFT application skill (default `tao-run-deft-aoi` for AOI; other `skills/applications/deft-*` skills for non-AOI cases) for the iterative data-improvement loop.
This skill **does not** re-implement AutoML or DEFT. It owns only the connective tissue: HPO spec inputs, the spec-handoff between AutoML and DEFT, and the post-DEFT AutoML re-run on the augmented dataset.
## Routing policy
- User asks to "run the AOI workflow" or "improve my AOI ChangeNet model" — **default to this skill**, not `tao-run-deft-aoi` directly. The bare DEFT loop is the inner stage of this pipeline.
- User wants AutoML and DEFT chained on the same model/dataset
- User says "AutoML at both ends", "tune HPs then DEFT", "warm-start DEFT", "AutoML before and after DEFT"
- User has an AutoML-tuned spec and asks how to feed it into DEFT
## When this skill does NOT apply