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name: clip_proxy_recovery description: Run a bounded soft-mode CLIP proxy recovery that logs contrastive training and zero-shot evaluation evidence.

CLIP Proxy Recovery

Use this skill when full web-scale CLIP pretraining is blocked but soft-mode recovery permits a declared proxy. Do not use it as proof of full CLIP performance.

Inputs

• Distiller attempt directory with module_plan.json and generated skills.
• Runtime handoff documenting package/model blockers.
• Tiny paired feature fixture or generated proxy data item.

Outputs

• recovery_result.json with numeric proxy metrics.
• generated_data_item.json, training_trace.json, experiment command log, and generated-skill invocation log.
• Mechanism checks for pair construction, contrastive loss, optimizer update, prompt classification, and source boundary.

Workflow

1. Read the module-plan target and runtime handoff.
2. Create or validate a tiny paired image-text feature dataset.
3. Run a real optimizer update on trainable scalar parameters using symmetric contrastive loss.
4. Classify held-out image vectors with prompt-derived class vectors.
5. Write auditable logs and mark the recovery as proxy/reduced.

Validation

Run the recovery harness and then validate_recovery_experiment.py on the attempt directory.

Limitations

This skill validates the mechanism, not the paper's full ImageNet-scale accuracy.