Evaluation Pipeline

What problem does it solve?

Design robust evaluation systems for large language models, enabling consistent benchmarks, human assessments, automated scoring, and regression testing across development and production environments.

Core Features & Use Cases

• Benchmark design: Create standardized evaluation datasets and scoring rubrics for reproducible model comparisons.
• LLM-as-judge patterns: Implement structured evaluation where stronger models judge weaker outputs with reproducible scoring.
• Quality rubrics & guardrails: Provide guidelines and automated checks to ensure reliability, fairness, and safety.
• CI/CD style evaluation: Integrate evaluation into development workflows to detect regressions across model updates and deployments.

Quick Start

Install this Skill into your Claude Code context and run the /setup command to generate evaluation contexts and a ready-to-run workspace.