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name: feature-evaluation description: "Extract CLIP features and evaluate downstream workflows safely." disable-model-invocation: true metadata: disco-role: operating license: MIT

CLIP Feature Evaluation

Use this sub-skill when an agent needs dataset-scale CLIP embeddings, normalized image/text features, similarity or search prototypes, .npz feature storage, or a downstream linear-probe evaluation plan.

Start Here

• For batch image embedding extraction, run python scripts/extract_image_features.py --help; it writes deterministic .npz outputs and only downloads weights if the selected named model is not already cached.
• For image/text embedding workflows, normalization rules, similarity/search prototypes, feature storage, validation checks, and scikit-learn linear probes, read references/feature-workflows.md.
• For intended research use, deployment boundaries, bias/fairness warnings, English-language limits, class taxonomy risk, and dataset licensing/scale caveats, read references/responsible-evaluation.md.
• For OOM, dtype/device mismatch, missing scikit-learn, unnormalized embeddings, dataset fixture/download issues, misleading linear-probe accuracy, and large-batch performance, read references/troubleshooting.md.

Scope Boundaries

This sub-skill owns feature extraction after a CLIP model/preprocess pair is available, model.encode_image, model.encode_text, feature normalization, evaluation storage, linear probes, small similarity/search prototypes, batch-size/device choices, and responsible evaluation caveats.

Do not use this sub-skill for first-time model loading, cache setup, image preprocessing mechanics, or single-image inference setup; route those steps to ../model-loading-inference/. Do not use it for prompt template design, prompt ensembling, or label wording strategy; route those decisions to ../prompt-engineering/. Treat large dataset and checkpoint downloads as explicit opt-in operations, not default evaluation steps.