rlxp-make-contract

Create or update study-scoped RL experiment contracts from approved task definitions.

1|Updated May 14, 2026
One-click install
npx skills add https://github.com/junhyekh/rlxp --skill rlxp-make-contract
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: rlxp-make-contract
Source: https://github.com/junhyekh/rlxp/tree/main/plugins/rl-experiment-assistant/skills/rlxp-make-contract
Command: npx skills add https://github.com/junhyekh/rlxp --skill rlxp-make-contract

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill turns a confirmed RL task into a bounded study contract so experiment planning, approval, and launch criteria stay explicit, comparable, and safe.

Core Features & Use Cases

  • Scoped contract creation: Defines task_id, study_id, metrics, guardrails, baseline, evaluation protocol, budget, hardware, and stopping rules.
  • Study initialization: Sets up the repo-local RLXP state needed for candidates, runs, audits, and reports.
  • Safety gating: Prevents training or autonomous execution until every required approval and constraint is recorded.
  • Use case: Use it when a research lead wants to formalize an RL experiment before any training starts or when a new metric change requires a fresh study scope.

Quick Start

Ask the assistant to create or update the RLXP study contract for my task using the approved metric, scope, budget, hardware, baseline, and evaluation details.

Frequently Asked Questions about rlxp-make-contract

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create a reinforcement learning experiment contract with safety guardrails before training starts?

An RL experiment contract formalizes task_id, study_id, metrics, guardrails, baseline, evaluation protocol, budget, hardware, and stopping rules into a bounded scope. It prevents training or autonomous execution from starting until every required approval and constraint is explicitly recorded.

What is the best way to formalize RL study scoping and baseline planning for audit handoff?

Study scoping for audit handoff turns an approved task definition and constraint set into a bounded contract. It establishes repo-local state for candidates, runs, audits, and reports, ensuring baseline agreement and metric scoping remain explicit, comparable, and safe.

How do I set up reinforcement learning training approval control with budget and hardware constraints?

Training approval control requires explicit budget, hardware, evaluation, and automation fields in the experiment contract. Safety gating blocks any training or autonomous execution until all required approvals and constraints are recorded in the study scope.

Can I update an existing RL experiment contract when a metric change requires a fresh study scope?

You can update an existing RL experiment contract when a metric change requires a fresh study scope. The contract creation process applies to both new task definitions and updates to existing studies, re-evaluating all required fields before enabling training.

Do I need a confirmed task definition and constraint set to initialize reinforcement learning study planning?

A confirmed task definition and constraint set are required before initializing RL study planning. The contract creation process needs explicit task, metric, guardrail, budget, hardware, baseline, evaluation, and automation fields to proceed with study initialization.

Why does my reinforcement learning training not start after study initialization?

Training fails to start when safety gating detects missing required fields. The contract must contain explicit task, metric, guardrail, budget, hardware, baseline, evaluation, and automation entries before training or autonomous execution can be enabled.