Skill UI

This tool allows users to systematically evaluate and rank the outputs of multiple agents within an AgentHub session. It supports metric-based evaluation (running custom commands for measurable metrics like speed or resource usage), or an advanced LLM Judge Mode. The LLM Judge compares results based on criteria like correctness, simplicity, and overall quality, helping users determine the best-performing agent for the task.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.

This guide provides a detailed walkthrough of the AAMAS peer review process. It covers everything from initial submissions and technical evaluation (correctness, significance, rigor) to the rebuttal phase, area-chair synthesis, and final decisions. Use this when planning academic submissions or understanding the rigorous criteria for publishing multi-agent systems research.

This guide details the rigorous, mathematically focused peer-review pipeline for top ML conferences like COLT (Conference on Learning Theory). It explains the double-anonymous review system, how reviewers assess correctness, significance, and novelty, and provides actionable advice on structuring rebuttals and interpreting final decisions for researchers aiming for top-tier academic publication.

Fine-tune LLMs on a single consumer GPU with Unsloth Core. Covers VRAM sizing, LoRA/QLoRA, GRPO/DPO post-training, chat-template correctness, and GGUF export.