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
Source commit fce3cca4170be75e82c64920654d56ee2811b005
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- Last checked
- Nov 9, 2026
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Repository context
- Repository stars (not skill ratings)
- 3,925
- Repository last updated
- Nov 9, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- computer-vision Declared skill tag
- training Declared skill tag
- evaluation Declared skill tag
- reproducibility Declared skill tag
- data-quality Declared skill tag
- engineering Derived category
- data Derived category
- devops Derived category
- research Derived category
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Browse files
BENCHMARK.mddoc · 8,661 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesSKILL.mdcore · 16,710 bytesassets/status.example.jsonasset · 1,721 bytesconfig/skillspector-baseline.yamlasset · 834 bytesevals/evals.jsonasset · 7,796 bytesreferences/failure-atlas.mddoc · 4,347 bytesreferences/port-gate.mddoc · 6,902 bytesreferences/postmortem.mddoc · 9,565 bytesreferences/recipe-fields.mddoc · 7,004 bytesreferences/recipe_spec.yamlasset · 4,176 bytesreferences/stacks.mddoc · 4,294 bytesreferences/verification-ladder.mddoc · 4,716 bytesscripts/domain_gap_report.pyruns · 14,106 bytesscripts/preprocess_parity.pyruns · 16,209 bytesscripts/render_status.pyruns · 6,942 bytesscripts/split_leakage_check.pyruns · 8,378 bytesskill-card.mddoc · 4,227 bytesskill.oms.sigasset · 7,725 bytes
Read excerpt
# 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.