How do I install phoenix-evals?

Run `npx skills add https://github.com/arize-ai/phoenix --skill phoenix-evals` in your terminal. You need to have run `npx skills init` once in your project first.

Which agent frameworks does phoenix-evals support?

phoenix-evals works with any agent framework supported by the Skills registry, including Claude Code, Cursor, GitHub Copilot, Cline, Codex, and Gemini CLI.

Is phoenix-evals free to use?

Yes. phoenix-evals is free to install and use. It is available from the open explainx.ai skill registry published by arize-ai.

Where can I read ratings and reviews for phoenix-evals?

Community ratings and review text appear on this explainx.ai skill page below the description. Reviews use a 1–5 scale and may include short written feedback from signed-in members.

Productivity

phoenix-evals▌

arize-ai/phoenix · updated Apr 8, 2026

$npx skills add https://github.com/arize-ai/phoenix --skill phoenix-evals

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summary

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

skill.md

Phoenix Evals

Build evaluators for AI/LLM applications. Code first, LLM for nuance, validate against humans.

Quick Reference

Task	Files
Setup	setup-python, setup-typescript
Decide what to evaluate	evaluators-overview
Choose a judge model	fundamentals-model-selection
Use pre-built evaluators	evaluators-pre-built
Build code evaluator	evaluators-code-python, evaluators-code-typescript
Build LLM evaluator	evaluators-llm-python, evaluators-llm-typescript, evaluators-custom-templates
Batch evaluate DataFrame	evaluate-dataframe-python
Run experiment	experiments-running-python, experiments-running-typescript
Create dataset	experiments-datasets-python, experiments-datasets-typescript
Generate synthetic data	experiments-synthetic-python, experiments-synthetic-typescript
Validate evaluator accuracy	validation, validation-evaluators-python, validation-evaluators-typescript
Sample traces for review	observe-sampling-python, observe-sampling-typescript
Analyze errors	error-analysis, error-analysis-multi-turn, axial-coding
RAG evals	evaluators-rag
Avoid common mistakes	common-mistakes-python, fundamentals-anti-patterns
Production	production-overview, production-guardrails, production-continuous

Workflows

Starting Fresh: observe-tracing-setup → error-analysis → axial-coding → evaluators-overview

Building Evaluator: fundamentals → common-mistakes-python → evaluators-{code|llm}-{python|typescript} → validation-evaluators-{python|typescript}

RAG Systems: evaluators-rag → evaluators-code-* (retrieval) → evaluators-llm-* (faithfulness)

Production: production-overview → production-guardrails → production-continuous

Reference Categories

Prefix	Description
`fundamentals-*`	Types, scores, anti-patterns
`observe-*`	Tracing, sampling
`error-analysis-*`	Finding failures
`axial-coding-*`	Categorizing failures
`evaluators-*`	Code, LLM, RAG evaluators
`experiments-*`	Datasets, running experiments
`validation-*`	Validating evaluator accuracy against human labels
`production-*`	CI/CD, monitoring

Key Principles

Principle	Action
Error analysis first	Can't automate what you haven't observed
Custom > generic	Build from your failures
Code first	Deterministic before LLM
Validate judges	>80% TPR/TNR
Binary > Likert	Pass/fail, not 1-5

Discussion

Product Hunt–style comments (not star reviews)

No comments yet — start the thread.

general reviews

Ratings

4.7★★★★★43 reviews

★★★★★Advait Lopez· Dec 28, 2024
Registry listing for phoenix-evals matched our evaluation — installs cleanly and behaves as described in the markdown.
★★★★★Noah Ghosh· Dec 16, 2024
Keeps context tight: phoenix-evals is the kind of skill you can hand to a new teammate without a long onboarding doc.
★★★★★Valentina Abebe· Dec 12, 2024
phoenix-evals has been reliable in day-to-day use. Documentation quality is above average for community skills.
★★★★★Pratham Ware· Dec 8, 2024
Solid pick for teams standardizing on skills: phoenix-evals is focused, and the summary matches what you get after install.
★★★★★Sakshi Patil· Nov 27, 2024
We added phoenix-evals from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
★★★★★Lucas Desai· Nov 19, 2024
Keeps context tight: phoenix-evals is the kind of skill you can hand to a new teammate without a long onboarding doc.
★★★★★Soo Patel· Nov 11, 2024
Useful defaults in phoenix-evals — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
★★★★★Kiara Kapoor· Nov 7, 2024
Registry listing for phoenix-evals matched our evaluation — installs cleanly and behaves as described in the markdown.
★★★★★Kiara Jain· Nov 3, 2024
phoenix-evals fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
★★★★★Valentina Khan· Oct 26, 2024
phoenix-evals reduced setup friction for our internal harness; good balance of opinion and flexibility.

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