executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
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reactive_controller_evaluation
reactive_controller_evaluationEvaluate GRAIL-style reactive behavioral clones with metrics, predictions, and mechanism checks.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentación de la Skill
GitHubname: reactive_controller_evaluation description: Evaluate GRAIL-style reactive behavioral clones with metrics, predictions, and mechanism checks.
reactive_controller_evaluation
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
- Run the separated goal/effect controller on held-out examples.
- Compare against a direct cloning baseline.
- Emit numeric metrics and mechanism checks for recovery validation.
Validation
Run python scripts/reactive_controller_evaluation.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.