
Arize Experiment Runner
Create, run, export and compare AI model evaluation experiments on Arize using the ax CLI.
- Repository
- github/awesome-copilot
- Stars
- 40,703
- Licence
- MIT
- Files
- 3 files
What it does
Once installed, the agent knows how to use the Arize ax command-line tool to manage experiments: listing and inspecting them, exporting every run with its model outputs and scores to a JSON file, and helping you compare how different models perform on the same dataset. It is written as step-by-step guidance for the agent, including how to fix common setup errors and a rule to never invent model outputs or scores.
Great for
- ML engineers who evaluate or benchmark LLM or model versions on Arize
- Teams that want to export experiment results and compare runs
- People already using Arize who want the agent to drive the ax CLI
Skip it if
- People without an Arize account or the ax CLI, since the skill cannot work without them
- Quick one-off model comparisons that do not need a tracked evaluation platform
- Anyone who expects the skill to hold credentials or call models by itself without their own API keys and setup
Try asking
- “Export all runs from my experiment 'summarizer-v2' on dataset 'support-tickets' to a JSON file”
- “List the 20 most recent experiments for the dataset 'qa-benchmark'”
- “Compare correctness scores between my two latest experiments on the same dataset”
- “Create an experiment from these run results and link it to my dataset”
Install in 3 steps
- 1
Download the skill
The ZIP contains the 3 original files, the MIT licence and an attribution note.
arize-experiment.zip - 2
Add it to your agent
Claude Code (all projects)
mkdir -p ~/.claude/skills && unzip arize-experiment.zip -d ~/.claude/skills/Claude Code / Agent SDK (this project only)
mkdir -p .claude/skills && unzip arize-experiment.zip -d .claude/skills/Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.
- 3
Ask for the task
No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.
Get better results
- Give the dataset and experiment names rather than IDs; names are easier to remember and the skill prefers them.
- Ask the agent to show the list of runs or a summary before comparing, so you can check the numbers come from real exports.
- Keep your API key in ax profiles rather than pasting it into chat; the skill itself forbids reading .env files or searching for credentials.
What's inside
- references/ax-profiles.md
- references/ax-setup.md
- SKILL.md
Redistributed unmodified from github/awesome-copilot under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.