Author or refine a Phoenix evaluator — code or LLM-as-a-judge — that scores a run's output. Trigger when the user wants to create a new evaluator, improve an existing one's logic or rubric, choose labels, or decide what to measure on a dataset or experiment. Do NOT trigger on: (1) manual prompt drafting (use playground), (2) running or comparing experiments themselves (use experiments), (3) cross-trace failure diagnosis with no evaluator in scope (use debug-trace).
How to install evaluators?
arize-ai/phoenix/evaluators
$npx -y skills add arize-ai/phoenix --skill evaluatorsInstalls into the current project.
›Prefer a prompt? Paste this to your agent
Run `npx skills use "https://github.com/arize-ai/phoenix" --skill "arize-ai/phoenix/evaluators"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/arize-ai/phoenix" that are relevant to the current task. Run `npx skills add "https://github.com/arize-ai/phoenix"` and select the relevant skills, then follow their instructions.
Related
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- webapp-testingToolkit for interacting with and testing local web applications using Playwright.Jul 2026
- playwright-best-practicesUse when writing Playwright tests, fixing flaky tests, debugging failures, implementing Page Object Model, configuring CI/CD, optimizing performance, mocking…Jul 2026
