arize-experiment
🤖 AI Summary
This skill enables programmatic creation and management of ML evaluation experiments, including exporting datasets, processing examples into experiment runs with model outputs and evaluations, and organizing runs under named experiments tied to specific dataset versions.
How to Install
Claude Code:
git clone --depth 1 https://github.com/github/awesome-copilot.git && cp awesome-copilot/skills/arize-experiment ~/.claude/skills/arize-experiment -r# Arize Experiment Skill
> **`SPACE`** — All `--space` flags and the `ARIZE_SPACE` 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. If you cannot call the API (missing SDK, missing credentials, network error), stop and tell the user what is needed before proceeding.
## List Experiments: `ax experiments list`
Browse experiments, optionally filtered by dataset. Output goes to stdout.
```bash
ax experiments list
ax experiments list --dataset DATASET_NAME --space SPACE --limit 20 # DATASET_NAME: name or ID (name preferred)
ax experiments list --cursor CURSOR_TOKEN
ax experiments list -o json
```
### Flags
| Flag | Type | Default | Description |
|------|------|---------|-------------|
| `--dataset` | string | none | Filter by dataset |
| `--limit, -l` | int | 15 | Max results (1-100) |
| `--cursor` | string | none | Pagination cursor from previous response |
| `-o, --output` | string | table | Output format: table, json, csv, parquet, or file path |
| `-p, --profile` | string | default | Configuration profile |
## Get Exp
Details
| Category | Data → data_proc |
| Source | github/awesome-copilot |
| SKILL.md | View on GitHub → |
| Repo Stars | ★ 35.6K |
| Est. per Skill | 712 (shared across 50 skills from this repo) |
| Difficulty | Intermediate |
| Risk Level | N/A |
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