Define datasets, rubrics, and quality thresholds for eval-driven development.
npx skills add https://github.com/SeaBrad72/sparkwright --skill evals-seabrad72
Please help me install this Agent Skill. Skill: evals Source: https://github.com/SeaBrad72/sparkwright/tree/main/skills/evals Command: npx skills add https://github.com/SeaBrad72/sparkwright --skill evals-seabrad72
This skill solves the problem of unreliable AI behavior by applying rigorous, test-driven discipline to probabilistic systems, ensuring that model-dependent features meet a measurable quality bar before deployment.
Use the evals skill to define a new evaluation plan for the current prompt by creating a dataset of inputs and a rubric for an independent judge model.
High-intent search queries and answers about installing and using this skill.
Eval-driven development applies test-driven discipline to probabilistic AI systems by establishing datasets, rubrics, and quality thresholds to ensure model-dependent features meet a measurable quality bar before deployment.
To prevent self-grading bias during prompt testing, you must define an independent judge model to evaluate outputs against a rubric, ensuring objective scoring rather than allowing the target model to grade its own responses.
You integrate AI testing gates into a CI/CD pipeline by defining evaluation datasets and quality thresholds that act as automated checkpoints, blocking deployment if probabilistic outputs or safety guardrails fail to meet the rubric.
Yes, you can prevent prompt injection and harmful outputs in production by integrating adversarial red-teaming tests as safety gates within your evaluation lifecycle, ensuring model features pass rigorous validation before release.
To set up an evaluation plan for a prompt, you need to create a dataset of inputs, define a scoring rubric, and configure an independent judge model to evaluate the outputs against your established quality thresholds.
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