Agent skill · nvidia
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.
What it needs
About 10k tokens when loaded.
What this skill does
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. Everything verifiable must be verified in Phase A, because the safety net disappears at the boundary. Once you are in Phase B, an implementation bug and a genuine domain mismatch produce identical symptoms: a mediocre number with a clean-looking loss curve. Two consequences that drive everything below: 1. Never skip the Phase A gate to "save time." It costs hours and it is the only thing that licenses trust in every number that follows. 2. Stop treating the paper's number as the target once the dataset changes. The target is beat the strongest cheap baseline on the customer's own test set, measured at a deliberately chosen operating point. Mode selection Read the request and pick a mode. Say which one you picked and why. Post-mortem mode — a run already happened and the result was wrong, disappointing, or suspiciously good. Go to references/postmortem.md. Do not start by rerunning anything. Walk the checks in likelihood order and find the gate that was skipped. Forward mode — no run yet, or starting over. Work Phase 0 → A → G → T → R → E below. Audit mode — a pipeline exists and works, someone wants it checked before it goes to a customer. Run the Phase A gate and the Phase G report against the existing artifacts, then the parity and leakage scripts. Skip the porting work. If the request is ambiguous, ask which of the three it is before doing anything expensive. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-validate-recipe-transfer