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name: sar_norm_adaptation_policy description: Use this skill when selecting trainable parameters for SAR-style test-time adaptation while freezing non-normalization model weights.

Norm Adaptation Policy

When To Use

Use this skill for SAR stable test-time adaptation work that needs: Configure a model or parameter set so test-time adaptation updates only normalization affine parameters. Do not use it as a generic ImageNet-C runner or as permission to read the original SAR repository during recovery.

Inputs

• model parameter metadata or small proxy parameter dict

Outputs

• trainable parameter selection and frozen parameter report

Workflow

1. Read the caller-provided stream, logits, parameter metadata, or entropy trace.
2. Apply the SAR-specific invariant documented in this skill: Stable wild TTA requires batch-agnostic normalization and restricted affine adaptation instead of full model updates.
3. Produce structured numeric evidence for recovery logs.
4. Keep proxy/reduced results explicitly labeled when the full ImageNet-C runtime is unavailable.

Validation

Run python scripts/norm_adaptation_policy.py when the script has a CLI, then run python -m pytest tests or the Distiller skill validator with --run-tests.

Limitations

This skill captures a reusable SAR mechanism. It does not contain pretrained weights, ImageNet-C data, or original repository code, and it should be combined with other SAR skills for end-to-end recovery.