Automates and optimizes LLM output testing and prompt evaluation using the Promptfoo framework.
The Promptfoo Evaluation skill enables developers to systematically test, compare, and refine LLM prompts within their Claude Code environment. By integrating the open-source Promptfoo CLI, this skill assists in configuring evaluation matrices, defining custom Python assertions, and implementing LLM-as-judge rubrics to ensure high-quality, consistent model outputs. It is particularly useful for teams needing to benchmark different models, validate few-shot examples, or monitor response quality across complex prompt iterations before production deployment.
Key Features
01Advanced validation using custom Python assertions and LLM-as-judge rubrics
02Echo provider integration for risk-free prompt rendering and debugging
03Automated prompt testing and model comparison across multiple providers
04147 GitHub stars
05Seamless creation and management of promptfooconfig.yaml files
06Support for multi-turn few-shot example management and chat-format prompts
Use Cases
01Benchmarking prompt performance between Claude, GPT, and other LLM models
02Validating complex output requirements using custom Python-based scoring
03Automating regression testing for prompts to prevent quality degradation
