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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Productivité · SKILL.md
goal_effect_induction
goal_effect_inductionInduce separated goal and effect rules from behavioral cloning traces while logging training evidence.
Independent GitHub snapshot · Automated classification · Original documentation governs
Documentation de la Skill
GitHubname: goal_effect_induction description: Induce separated goal and effect rules from behavioral cloning traces while logging training evidence.
goal_effect_induction
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
- Induce threshold goal rules from state variables.
- Induce effect rules that map goal error to elevator actions.
- Optionally run a tiny scalar optimizer step for reduced recovery evidence.
Validation
Run python scripts/goal_effect_induction.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.