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name: bar_recovery_evaluation description: Produce validator-ready BAR recovery metrics and mechanism checks for full or declared reduced black-box reprogramming experiments.

BAR Recovery Evaluation

Use this skill at the end of a BAR recovery run to turn predictions, labels, training traces, runtime blockers, and generated-skill invocation evidence into an auditable recovery result.

Do not use it to justify a proxy experiment that did not run masked embedding, multi-label mapping, and a black-box query-only optimization step.

Inputs

• Predictions and labels.
• Training trace with params_before, params_after, loss_before, and loss_after.
• Module-plan fast recovery target.
• Runtime blockers and source-boundary notes.

Outputs

• Accuracy and loss improvement.
• Mechanism checks for BAR components.
• A recovery-result dictionary compatible with validate_recovery_experiment.py.

Workflow

1. Compute accuracy from predictions and labels.
2. Check that parameters changed when an optimizer step is claimed.
3. Check loss-before/loss-after fields and query count.
4. Mark reduced/proxy booleans explicitly when full medical-data recovery is unavailable.
5. Copy target metadata from module_plan.json.fast_recovery_target so the recovery target cannot drift.

Validation

Run python tests/test_recovery_evaluation.py or validate_skill_tree.py --run-tests.

Limitations

This skill evaluates evidence; it does not run BAR training by itself and cannot rescue missing executable command logs.