agent-quality-patterns

What problem does it solve?

This Skill helps engineers and managers improve the quality of AI agents and LLM-powered features, bridging the gap between prototype and production.

Core Features & Use Cases

• Self-Critique Loop: Implement self-critique to improve output quality.
• Evaluator-Optimizer Pipeline: Score and refine AI outputs for higher accuracy.
• LLM-as-Judge: Use a separate LLM for consistent quality evaluation.
• Golden Dataset Evals: Test AI agents against a curated set of inputs.
• Use Case: When preparing an AI agent for production, use this Skill to establish a robust evaluation process, ensuring reliability and correctness.

Quick Start

Analyze the quality of your AI agent using the agent-quality-patterns skill.