Evaluate and compare LLM prompts with red-teaming and vulnerability scanning.
npx skills add https://github.com/lucidlabs-hq/agent-kit --skill promptfoo
Please help me install this Agent Skill. Skill: promptfoo Source: https://github.com/lucidlabs-hq/agent-kit/tree/main/.claude/skills/promptfoo Command: npx skills add https://github.com/lucidlabs-hq/agent-kit --skill promptfoo
LLM prompt design and evaluation is often manual and error prone. PromptFoo provides a structured framework to systematically test, compare, and improve prompts, reducing bugs and inconsistencies across projects.
Run a full prompt evaluation against your Mastra prompts to identify improvement opportunities.
High-intent search queries and answers about installing and using this skill.
To systematically evaluate and compare LLM prompts, you can use structured test suites to run baseline, edge case, and regression tests across different models and configurations. This framework identifies prompt strengths and weaknesses to reduce inconsistencies.
Red-teaming in prompt testing involves applying integrated vulnerability scans to uncover prompts that could lead to unsafe outputs or the leakage of sensitive information. This process detects and mitigates prompt-related risks across different versions.
You can track LLM prompt improvements over iterations by using a self-learning workflow that documents changes and tracks metrics over time. This enables continuous prompt optimization in production environments.
Yes, this prompt evaluation framework supports Claude MCP server integration, allowing you to easily incorporate systematic prompt testing and vulnerability scanning into your existing AI stacks.
The best way to identify prompt-related risks is to apply red-teaming and vulnerability scanning across prompt versions. Integrated checks help detect prompts that could cause unsafe outputs or sensitive information leakage.
To set up reference test suites for LLM evaluation, you configure baseline, edge case, security, and regression tests to validate your prompts across various models and configurations. Running a full evaluation identifies improvement opportunities.
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