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name: sil_recovery_evaluation description: Validate Self-Imitation Learning recovery evidence for source boundaries, executable metrics, and mechanism-faithful proxy checks.

SIL Recovery Evaluation

Use this skill after running a SIL recovery experiment. It is designed for soft-mode reduced recoveries and full actor-critic recoveries that need auditable acceptance checks before analysis.

Do not use this skill to waive missing executable evidence; it should reject missing command logs, missing optimizer-step evidence, or original-repository source leakage.

Inputs

• Recovery result JSON.
• Experiment command log JSON.
• Generated skill invocation log JSON.
• Source manifest JSON.
• Optional training trace JSON.

Outputs

• Validation JSON with ok, errors, warnings, and metric summary.
• Mechanism checklist suitable for Distiller analysis.

Workflow

1. Check that source manifest entries do not include the original source repository during recovery.
2. Check that a successful command produced the recovery result.
3. Check that generated skills were invoked or cross-checked.
4. Check that proxy mechanism booleans include replay insertion, positive-advantage loss, optimizer step, and parameter change.
5. Report metric and loss-change evidence.

Validation

Run:

python tests/test_evaluate_recovery.py

The test validates a minimal passing proxy result and a failing result with missing optimizer evidence.

Limitations

• This is a SIL-specific supplement to Distiller's generic validate_recovery_experiment.py; run both when finishing a recovery.

Source-Boundary Negative Test

The recovery source manifest should not include the original implementation repository path, even as a convenience reference. Record source-boundary status without embedding forbidden paths in recovery evidence.