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name: score_sde_recovery_harness description: Assemble executable soft-mode recovery artifacts for reduced score-SDE training and mechanism validation.

Score SDE Recovery Harness

Use this skill after the score-SDE schedule, loss, and sampling skills exist and a runtime handoff has established whether full training is feasible. It creates validator-compatible recovery artifacts for a declared reduced/proxy run when full image training is blocked.

Inputs

• Attempt directory path.
• Runtime handoff JSON.
• Module plan with the declared fast recovery target.
• Generated skill root containing the schedule, loss, and predictor-corrector skills.

Outputs

• recovery/recovery_result.json with target metadata and metrics.
• A training trace at recovery/logs/training_trace.json with loss before/after and params_before/params_after.
• recovery/logs/generated_data_item.json and recovery/logs/generated_skill_invocations.json.
• Mechanism checks for continuous time sampling, perturbation scores, optimizer execution, and predictor-corrector execution.

Workflow

1. Read the module plan and runtime handoff before selecting reduced recovery.
2. Build a deterministic synthetic Gaussian-mixture proxy batch.
3. Import and call the generated schedule and loss scripts to compute perturbation targets and run one optimizer step.
4. Import and call the predictor-corrector script as a reverse-dynamics cross-check.
5. Write all recovery artifacts and command metadata from the executable run.
6. Keep full-runtime flags false when deep-learning packages or full datasets are unavailable.

Validation

Run python scripts/run_recovery.py --attempt-dir <attempt_dir> --generated-skills-root <root> and then run the Distiller recovery validator.

Limitations

This harness is valid only as a soft-mode reduced recovery unless the runtime handoff proves full model training is available and the harness is extended accordingly.