rlxp-autoloop

What problem does it solve?

This Skill helps you resume and automate an approved reinforcement-learning experiment loop without mixing tasks, violating scope, or launching unsafe runs.

Core Features & Use Cases

• State-aware iteration: Reads the current .rlxp/ state, contract, ledger, reports, candidates, and run artifacts before deciding the next action.
• Safe experiment control: Validates launch gates, enforces budget and scope checks, and allows training only through the bundled autoloop runner.
• Evidence-driven decisions: Compares results against the incumbent, classifies outcomes, proposes the next candidate, and updates study artifacts.
• Use Case: Continue an in-progress RL study where the assistant must analyze results, decide whether the latest run is acceptable, and either launch the next approved candidate or stop at a boundary.

Quick Start

Ask the assistant to resume the RLXP autoloop for the active task and continue iterating until the approved budget or stop rule is reached.