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Cekura Metric Design

Creates custom AI and machine-learning system evaluation metrics and workflows.

5|1|Updated Mar 6, 2026
Search Tags:#prompt engineering#quality evaluation#custom code#metric design#ai voice agent#llm judge
One-click install
npx skills add https://github.com/cekura-ai/claude-skills --skill cekura-metric-design
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Cekura Metric Design
Source: https://github.com/cekura-ai/claude-skills/tree/main/plugins/cekura-metrics/skills/metric-design
Command: npx skills add https://github.com/cekura-ai/claude-skills --skill cekura-metric-design

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill guides users through the complex process of creating, testing, and iterating on metrics that accurately evaluate AI voice agent performance, ensuring robust and meaningful quality assessments.

Core Features & Use Cases

  • Metric Creation Workflow: Follows a structured process from context gathering to iteration for designing effective metrics.
  • LLM Judge & Custom Code: Supports both LLM-based evaluation and custom Python code for diverse metric needs.
  • Prompt Engineering Guidance: Provides proven prompt structures and best practices for clear and consistent metric evaluation.
  • Use Case: A product manager needs to define a new metric to track how well an AI agent handles customer complaints. This Skill will guide them through understanding the complaint scenario, writing an LLM prompt to evaluate agent responses, and deploying it.

Quick Start

Use the metric-design skill to create a new LLM judge metric for evaluating agent empathy.

Frequently Asked Questions about Cekura Metric Design

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

FAQPage Schema
How do I design metrics to evaluate AI voice agent performance?▼

Designing metrics for AI voice agent performance involves a structured workflow from context gathering to iteration, utilizing both LLM judge prompts and custom Python code to ensure robust quality assessments.

What is an LLM judge and how does it work for evaluating agent responses?▼

An LLM judge evaluates AI agent responses by using engineered prompts to score specific metrics. This approach provides structured prompt engineering guidance to ensure clear, consistent, and automated quality evaluation of voice agent interactions.

Can I use custom Python code instead of an LLM judge for metric evaluation?▼

Yes, you can use custom Python code for metric evaluation. This Skill supports both LLM-based evaluations and custom Python code implementations, allowing you to choose the best approach for your diverse metric needs.

What is the best way to create a new metric to track how well an AI handles complaints?▼

The best way to create a complaint-handling metric is to follow a structured creation workflow: understand the complaint scenario, write an LLM prompt or custom code to evaluate agent empathy, and iteratively refine the evaluation criteria.

Do I need prompt engineering experience to evaluate AI voice agent quality?▼

You do not need extensive prompt engineering experience, as this Skill provides proven prompt structures and best practices to guide you in writing clear and consistent prompts for evaluating AI voice agent quality.

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