agent-evaluation

Name: agent-evaluation
Availability: InStock
Author: teegin-g

Evaluate and optimize LLM agent outputs using MLflow APIs.

Updated Jan 30, 2026

Search Tags:#llm #evaluation #tracing #scorers #mlflow #datasets #agent-evaluation

Ready to run

npx skills add https://github.com/teegin-g/Slopcast --skill agent-evaluation-teegin-g

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: agent-evaluation
Source: https://github.com/teegin-g/Slopcast/tree/main/.agents/skills/agent-evaluation
Command: npx skills add https://github.com/teegin-g/Slopcast --skill agent-evaluation-teegin-g

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Evaluates and improves the performance and reliability of LLM agents by applying MLflow-based evaluation, tracing, and scoring to identify weaknesses, optimize tool selection, reduce costs, and fix inaccuracies in agent outputs.

Core Features & Use Cases

End-to-end evaluation workflows using MLflow APIs (datasets, scorers, traces) to measure agent quality; step-by-step guidance for both full workflows and individual components like tracing setup, dataset creation, and evaluation execution.
Requires loading the instrumenting-with-mlflow-tracing skill before starting any work and provides templates and scripts to standardize evaluation pipelines.

Quick Start

Run an initial evaluation by preparing an MLflow-enabled environment, loading the agent, and following the four-step workflow to understand, configure scorers, prepare datasets, and run evaluation.

Frequently Asked Questions about agent-evaluation

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

FAQPage Schema

How do I evaluate and optimize LLM agent outputs using MLflow?▼

You can evaluate LLM agent outputs using MLflow by applying tracing setup, dataset creation, scorer definition, and execution scripts to identify weaknesses, optimize tool selection, and fix inaccuracies in agent workflows.

What is the best way to set up MLflow tracing for GenAI agents?▼

MLflow tracing for GenAI agents requires loading the instrumenting-with-mlflow-tracing skill first to integrate tracing APIs, which then enables the evaluation scripts to capture and measure end-to-end agent performance.

How do I create datasets and define scorers for LLM evaluation?▼

You can create datasets and define scorers for LLM evaluation by utilizing the provided templates and scripts in the scripts directory, which standardize the configuration of MLflow evaluation pipelines for component-level tasks.

Can I use MLflow evaluation workflows for individual agent components instead of end-to-end tasks?▼

Yes, MLflow evaluation workflows apply to both end-to-end agent workflows and individual components, providing step-by-step guidance for tracing setup, dataset preparation, scorer configuration, and evaluation execution.

Does MLflow agent evaluation require any specific environment setup or dependencies?▼

MLflow agent evaluation requires access to MLflow APIs, an MLflow-enabled environment, and prior loading of the instrumenting-with-mlflow-tracing skill to successfully execute the four-step evaluation workflow.

Why should I use MLflow scorers instead of other LLM evaluation methods?▼

MLflow scorers provide standardized evaluation pipelines that leverage tracing integration to identify agent weaknesses, reduce operational costs, and fix output inaccuracies across both full workflows and component-level tasks.