build-eval

Name: build-eval
Availability: InStock
Author: yzavyas

Design and run evaluation harnesses for LLM agents and systems.

7|3|Updated Dec 1, 2025

Search Tags:#metrics #multi-agent #tests #evaluate #evals #DeepEval #RAGAS

Ready to run

npx skills add https://github.com/yzavyas/claude-1337 --skill build-eval

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: build-eval
Source: https://github.com/yzavyas/claude-1337/tree/main/plugins/eval-1337/skills/build-eval
Command: npx skills add https://github.com/yzavyas/claude-1337 --skill build-eval

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This skill provides a structured framework to design, validate, and compare evaluations for LLM agents, multi-agent systems, skills, MCP servers, and prompts, reducing ambiguity in measurement and alignment.

Core Features & Use Cases

Support for end-to-end evaluation metrics (TaskCompletion, ToolCorrectness, pass@k, iterative metrics)
Frameworks integration: DeepEval, Braintrust, RAGAS, Promptfoo for flexible scoring
Use cases include: validating agent coordination, benchmarking MCP server reliability, and developing reusable eval templates.

Quick Start

Run a baseline evaluation harness against your codebase by assembling a dataset of test cases, choosing a framework, and executing the evaluator. For example: create a dataset with agent tasks, select DeepEval metrics, and run your evaluation harness.

Frequently Asked Questions about build-eval

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

FAQPage Schema

How do I evaluate LLM agent effectiveness and multi-agent coordination?▼

Evaluating LLM agents requires building reproducible test harnesses with clearly defined activation criteria and labeled metrics. You can measure agent effectiveness and coordination using iterative metrics like TaskCompletion and ToolCorrectness.

What is the best way to build a test suite for multi-agent systems?▼

Building a test suite for multi-agent systems involves assembling a dataset of test cases, selecting an evaluation framework like DeepEval or RAGAS, and executing the evaluator to generate traceable, reproducible results.

Can I use DeepEval or RAGAS to benchmark MCP server reliability?▼

Yes, you can benchmark MCP server reliability by integrating DeepEval, RAGAS, Promptfoo, or Braintrust into your evaluation harness. These frameworks provide flexible scoring for measuring server performance.

Does evaluating prompts require a specific framework or labeled metrics?▼

Evaluating prompts requires labeled metrics to reduce ambiguity in alignment. You must choose a framework such as DeepEval, Braintrust, or RAGAS and establish reproducible harnesses with traceable results.

What metrics should I use for LLM agent evaluation?▼

LLM agent evaluation uses end-to-end metrics including TaskCompletion, ToolCorrectness, and pass@k. These labeled metrics validate agent coordination and provide reproducible measurement of task effectiveness.

Why do I need reproducible harnesses for evaluating prompts and agents?▼

Reproducible harnesses are needed to ensure traceable results when validating LLM agents and prompts. They provide a structured framework to design, validate, and compare evaluations, reducing ambiguity in measurement.