---

name: trace_schema_preparation description: Prepare fixed-rate behavioral cloning trace examples for GRAIL-style reduced recovery experiments.

trace_schema_preparation

Use this skill when recovering Bain and Sammut style behavioural cloning without the original simulator repository. It is not a general reinforcement-learning skill; it targets trace-driven reactive cloning with explicit GRAIL-style separation.

Inputs

• JSON-compatible trace examples or prepared examples, depending on the script.
• Current-attempt artifacts only; do not read any original source repository.

Outputs

• Deterministic JSON-compatible structures that can be consumed by downstream recovery modules.

Workflow

1. Validate required process variables.
2. Derive goal-elevation and elevator-action labels.
3. Preserve provenance and avoid adding downstream predictions.

Validation

Run python scripts/trace_schema_preparation.py --help where applicable and run the tests through the Distiller skill-tree validator with --run-tests.

Limitations

The scripts implement a reduced proxy for the paper mechanism. They validate trace preparation, GRAIL separation, compactness, and reactive action prediction; they do not recreate the unavailable flight simulator.