Agent skill · data analytics · github
arize-experiment
Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI. Use when the user mentions create experiment, run experiment, compare models, model performance, evaluate AI, experiment results, benchmark, A/B test models, or measure accuracy.
Why this skill is useful
Provides specific commands and workflows for managing Arize experiments that the AI wouldn't reliably generate on its own.
What it needs
Requires ax installed locally. Requires arize account access. About 11k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
Arize Experiment Skill SPACE — All --space flags and the ARIZESPACE env var accept a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax spaces list. Concepts Experiment = a named evaluation run against a specific dataset version, containing one run per example Experiment Run = the result of processing one dataset example -- includes the model output, optional evaluations, and optional metadata Dataset = a versioned collection of examples; every experiment is tied to a dataset and a specific dataset version Evaluation = a named metric attached to a run (e.g., correctness, relevance), with optional label, score, and explanation The typical flow: export a dataset → process each example → collect outputs and evaluations → create an experiment with the runs. Prerequisites Proceed directly with the task — run the ax command you need. Do NOT check versions, env vars, or profiles upfront. If an ax command fails, troubleshoot based on the error: command not found or version error → see references/ax-setup.md 401 Unauthorized / missing API key → run ax profiles show to inspect the current profile. If the profile is missing or the API key is wrong, follow references/ax-profiles.md to create/update it. If the user doesn't have their key, direct them to https://app.arize.com/admin > API Keys Space unknown → run ax spaces list to pick by name, or ask the user Project unclear → ask the user, or run ax projects list -o json --limit 100 and present as selectable options Security: Never read .env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for LLM provider keys. If credentials are not available through these channels, ask the user. CRITICAL — Never fabricate outputs: When running an experiment, you MUST call the real model API specified by the user for every dataset example. Never fabricate, simulate, or hardcode model outputs, latencies, or evaluation scores. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/arize-experiment