Individual skill

tao-validate-recipe-transfer

Port a published computer vision paper's official code and training recipe onto a customer's own dataset, or diagnose why such a transfer produced bad numbers. Use this whenever someone wants to reproduce a CV paper, run a paper's repo on their own images, fine-tune a published detection/segmentation/classification/keypoint model on customer data, adapt a training recipe to a new dataset, or figure out why a fine-tuned vision model scores well on validation but fails in production. Also use for post-mortems on any failed or disappointing CV training run, and whenever a user mentions mAP that looks too good, a model that "worked in training but not in deployment", or transferring hyperparameters from a paper to their own data. Trigger even if the user only says "train a model on my dataset" and a published architecture or repo is involved.

skills/tao-validate-recipe-transfer/SKILL.md

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Nov 9, 2026
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# CV Recipe Transfer

Port published CV work onto customer data without inheriting the silent bugs that make
the resulting numbers meaningless.

## The core idea

This work splits into two phases with **fundamentally different epistemic status**:

- **Phase A (Port)** — get the official code running faithfully. There is external ground
  truth here: the authors' released checkpoint and reported number.
- **Phase B (Transfer)** — move the recipe onto customer data. There is **no** external
  ground truth. The paper's number stops being meaningful the moment the dataset changes.
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