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designing-evaluations-for-agents

Design evaluation frameworks with metrics and pass/fail thresholds for LLM-based agents.

Updated Feb 26, 2026
Search Tags:#testing#quality assurance#llm#benchmarking#framework design#agent evaluation
One-click install
npx skills add https://github.com/maltemd/hoover-content-design-system --skill designing-evaluations-for-agents-maltemd
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: designing-evaluations-for-agents
Source: https://github.com/maltemd/hoover-content-design-system/tree/main/skills/mcp-and-agents/designing-evaluations-for-agents
Command: npx skills add https://github.com/maltemd/hoover-content-design-system --skill designing-evaluations-for-agents-maltemd

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a structured approach to designing comprehensive evaluation frameworks for AI agents, ensuring their behavior, quality, and safety are rigorously measured.

Core Features & Use Cases

  • Define Evaluation Criteria: Establish clear metrics and pass/fail conditions.
  • Design Test Scenarios: Create diverse test cases including happy paths, edge cases, and adversarial attacks.
  • Use Case: When developing a new customer support chatbot, use this Skill to design a test suite that covers common user queries, potential escalations, and attempts to elicit harmful responses, ensuring the bot is both helpful and safe.

Quick Start

Use the designing-evaluations-for-agents skill to create a new evaluation framework for a code generation agent.

Frequently Asked Questions about designing-evaluations-for-agents

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

FAQPage Schema
How do I design an evaluation framework for LLM agents?▼

Designing an LLM agent evaluation framework requires defining agent capabilities, success criteria, input scenarios, and expected behaviors to rigorously measure quality and safety.

What metrics should I use for benchmarking AI agents?▼

Benchmarking AI agents involves establishing measurable metrics and pass/fail thresholds based on defined success criteria and expected behaviors to quantify agent performance.

How do I create test scenarios for a customer support chatbot?▼

Creating chatbot test scenarios involves designing diverse test cases covering happy paths, edge cases, and adversarial attacks to ensure helpful and safe responses.

What's the best way to structure quality assurance tests for code generation agents?▼

Structuring quality assurance for code generation agents requires defining specific agent capabilities and input scenarios to build a comprehensive test suite.

Can I use this approach to test adversarial attacks on AI agents?▼

Yes, designing evaluation frameworks includes creating specific test scenarios for adversarial attacks to ensure the AI agent handles potential escalations safely.

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