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---
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.
