Offline Recovery Harness

Use this skill when full D4RL packages or datasets are unavailable but soft-mode recovery permits a declared mechanism-faithful proxy. The harness should still perform executable work: validate a fixed transition dataset, run a parameter update from that dataset, compute normalized scores, and save mechanism evidence.

Inputs

• module_plan.json containing the fast recovery target.
• environment/runtime_handoff.json describing package blockers and allowed sources.
• Generated skill root containing dataset validation, score normalization, and taxonomy skills.
• An output recovery directory.

Outputs

• recovery_result.json with numeric metrics and target metadata.
• logs/generated_data_item.json with transition content and provenance.
• logs/training_trace.json with loss_before, loss_after, params_before, and params_after.
• Invocation logs proving generated skills were used.

Workflow

1. Read the module plan target and runtime handoff.
2. Build a tiny fixed offline chain dataset with explicit synthetic-proxy provenance.
3. Validate the dataset through offline_dataset_protocol.
4. Classify proxy metadata through benchmark_property_taxonomy.
5. Fit a one-parameter linear Q approximation with a deterministic gradient update.
6. Compute raw and normalized return through d4rl_score_normalization.
7. Save mechanism checks: no online collection, fixed dataset consumed, reduced training executed, optimizer step changed parameters, full D4RL packages unavailable, and normalized scoring used.
8. Keep full-runtime flags false when the runtime handoff reports missing packages.

Validation

Run the harness script used by the recovery attempt, then run Distiller's validate_recovery_experiment.py on the attempt directory.

Limitations

This harness is a reduced proxy, not a full D4RL benchmark run. It should be accepted only in soft mode after full recovery is blocked and the experiment gate passes.