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name: robustness_benchmark_protocol description: Validate clean and shifted robustness benchmark protocols with class-overlap and metric-gap contracts.

Robustness Benchmark Protocol

Use this skill when an experiment needs an ImageNet-R-style robustness protocol: a clean control split, a shifted split, a shared label set, and an explicit metric direction. Do not use it to claim full ImageNet reproduction; it only validates benchmark structure and computes gap semantics.

Inputs

• Clean examples as dictionaries with id, label, and optional features.
• Shifted examples with the same label vocabulary.
• Metric name and direction: lower_is_better for error or higher_is_better for accuracy.

Outputs

• A benchmark specification with clean classes, shifted classes, overlap status, and metric direction.
• A gap value computed as shifted minus clean for error metrics or clean minus shifted for accuracy metrics.

Workflow

1. Load or construct clean and shifted example lists.
2. Verify that the shifted labels are a subset of the clean labels.
3. Record the shift type and class-overlap evidence.
4. Compute clean/shifted metric gaps using direction-aware semantics.
5. Reject protocols that hide class mismatch or metric direction.

Validation

Run python tests/test_protocol.py or validate the tree with the Distiller skill validator.

Limitations

This skill validates protocol mechanics. It does not download ImageNet-R, train models, or certify that a proxy dataset is equivalent to the paper benchmark.