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name: pinn_recovery_evaluation description: Convert PINN traces into numeric proxy metrics and mechanism checks for validator-ready recovery evidence.

PINN Recovery Evaluation

Use this skill after a PINN experiment command has produced a data item, training trace, and generated-skill invocation evidence. It decides whether the trace proves the declared full or reduced target. Do not use it to train the model or compute PDE derivatives.

Inputs

• Module-plan target metadata.
• Training trace with loss components and before/after parameters.
• Data item and source-manifest metadata.
• Generated-skill invocation evidence.

Outputs

• Numeric metrics such as loss_reduction and relative_loss_reduction.
• Mechanism checks covering data construction, residual computation, composite loss, optimizer update, source boundary, and reduced/full runtime status.
• Recovery-result fields aligned with module_plan.json.fast_recovery_target.

Workflow

1. Load the training trace and compute loss-reduction metrics.
2. Confirm parameter or optimizer-state change evidence.
3. Build mechanism checks for all claimed modules.
4. Preserve target metadata exactly from the module plan.
5. Emit validator-compatible recovery fields.

Validation

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

Limitations

This skill accepts reduced recovery only when mechanism checks are explicit. Full paper-scale claims require separate runtime evidence.