evaluation-frameworks

What problem does it solve?

This Skill provides structured methodologies and tools to rigorously evaluate the quality, performance, and reliability of AI models, code, and agents.

Core Features & Use Cases

• LLM Evaluation: Assess AI responses based on accuracy, relevance, and helpfulness using rubrics and LLM-as-Judge.
• Code Quality Assessment: Analyze code for correctness, design, security, performance, maintainability, and testing using automated metrics.
• Agent Benchmarking: Define and run benchmarks to measure agent task completion success, accuracy, and efficiency.
• A/B Testing: Design and analyze experiments to compare different AI models or prompts.
• Use Case: A team developing a new AI assistant can use this Skill to benchmark its performance against existing models, identify areas for improvement in its responses, and ensure code quality before deployment.

Quick Start

Use the evaluation-frameworks skill to assess the quality of the latest code commit.