Can I use custom Python scripts for evaluation?

Yes, this skill supports defining custom Python assertions to calculate specific metrics such as text reduction ratios, word counts, or complex logical matches.

Does it support LLM-as-judge?

Yes, it includes detailed implementation patterns for llm-rubric assertions where a grader model evaluates the quality of an output based on your specific criteria.

How does this skill help with Claude Code?

It provides specialized guidance and configuration patterns to set up Promptfoo within your project, making it easier to run evaluations and manage configurations directly within your workflow.

Promptfoo is an open-source CLI tool used for testing and evaluating LLM outputs against predefined requirements, benchmarks, and assertions.

What is the 'echo' provider used for?

The echo provider allows you to preview how your prompts are rendered with variables and few-shot examples without consuming API tokens or making network calls.

Promptfoo LLM Evaluation

Name: Promptfoo LLM Evaluation
Author: daymade

bydaymade

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147

Seguridad y Pruebas

Automates and optimizes LLM output testing and prompt evaluation using the Promptfoo framework.

Acerca de

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.

Características Principales

Advanced validation using custom Python assertions and LLM-as-judge rubrics
Echo provider integration for risk-free prompt rendering and debugging
Automated prompt testing and model comparison across multiple providers
147 GitHub stars
Seamless creation and management of promptfooconfig.yaml files
Support for multi-turn few-shot example management and chat-format prompts

Casos de Uso

Benchmarking prompt performance between Claude, GPT, and other LLM models
Validating complex output requirements using custom Python-based scoring
Automating regression testing for prompts to prevent quality degradation

Acerca de

Características Principales

Advanced validation using custom Python assertions and LLM-as-judge rubrics
Echo provider integration for risk-free prompt rendering and debugging
Automated prompt testing and model comparison across multiple providers
147 GitHub stars
Seamless creation and management of promptfooconfig.yaml files
Support for multi-turn few-shot example management and chat-format prompts

Casos de Uso

Benchmarking prompt performance between Claude, GPT, and other LLM models
Validating complex output requirements using custom Python-based scoring
Automating regression testing for prompts to prevent quality degradation