How does the A/B testing handle statistical significance?

The framework uses a paired t-test design with recommended sample sizes (minimum 12 runs) to achieve 80% statistical power for detecting significant performance gains.

Is it expensive to run agent-led training?

Version 2.3 is cost-optimized, reducing compute requirements significantly to ~70-95 Compute Units and projecting an API cost of approximately $3.50 per training run.

What metrics does this skill track?

It evaluates Profit Factor, Sharpe Ratio, Maximum Drawdown, and training consistency to determine if agent consultations provide measurable model improvement.

Can I use this with any RL environment?

While pre-configured for Alpaca trading and vectorized environments, the validation logic and agent consultation patterns can be adapted for various custom RL frameworks.

RL Agent Validation Framework

Name: RL Agent Validation Framework
Author: smith6jt-cop

bysmith6jt-cop

•

Data Science & ML

Conducts rigorous A/B testing and cost-benefit analysis to validate the impact of Claude-driven agents on reinforcement learning model performance.

This skill provides a specialized infrastructure for evaluating whether integrating Claude agents into reinforcement learning (RL) training cycles actually improves model predictive power. It automates A/B testing between native PPO trainers and multi-agent systems, quantifying improvements in profit factor, Sharpe ratio, and drawdown while managing API costs and compute resources. It is an essential tool for quantitative researchers and developers who need empirical evidence before deploying autonomous agents in live trading or high-stakes financial environments.

Key Features

01Robust error handling with 5-strategy JSON parsing and rate-limit management

02Multi-agent integration for Hyperparameter Tuners, Risk Analysts, and Reward Engineers

03Comprehensive cost-benefit and break-even analysis for LLM API expenditures

04Incremental save and resume logic optimized for Google Colab and A100 GPU workflows

05Automated A/B testing protocol comparing baseline vs. agent-enhanced RL models

061 GitHub stars

Use Cases

01Determining if LLM agents provide a statistically significant boost to trading model Profit Factor

02Optimizing API consumption and compute costs during large-scale RL training experiments

03Debugging agent-led hyperparameter adjustments and reward engineering logic

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add smith6jt-cop/skills_registry agent-validation-experiment

For use in Claude.ai and ChatGPT

Download Skill