mcpbeat

Arize Experiment

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.

6k tokens
context cost
the whole folder, loaded on every use
3
files
instructions only
0
copies elsewhere
how many repositories repackaged it
37394
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/github/awesome-copilot --skill arize-experiment

What comes with it

6 134 bytes besides the instruction
references/ax-profiles.md
references/ax-setup.md

The instruction itself

27 sections, as written by the author

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.

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 Experiment: ax experiments get

Quick metadata lookup -- returns experiment name, linked dataset/version, and timestamps.

ax experiments get NAME_OR_ID
ax experiments get NAME_OR_ID -o json
ax experiments get NAME_OR_ID --dataset DATASET_NAME --space SPACE   # required when using experiment name instead of ID

Flags

| Flag | Type | Default | Description |

|------|------|---------|-------------|

| NAME_OR_ID | string | required | Experiment name or ID (positional) |

| --dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |

| --space | string | none | Space name or ID (required if using dataset name instead of ID) |

| -o, --output | string | table | Output format |

| -p, --profile | string | default | Configuration profile |

Response fields

| Field | Type | Description |

|-------|------|-------------|

| id | string | Experiment ID |

| name | string | Experiment name |

| dataset_id | string | Linked dataset ID |

| dataset_version_id | string | Specific dataset version used |

| experiment_traces_project_id | string | Project where experiment traces are stored |

| created_at | datetime | When the experiment was created |

| updated_at | datetime | Last modification time |

Export Experiment: ax experiments export

Download all runs to a file. By default uses the REST API; pass --all to use Arrow Flight for bulk transfer.

# EXPERIMENT_NAME, DATASET_NAME: name or ID (name preferred)
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE
# -> experiment_abc123_20260305_141500/runs.json

ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --all
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --output-dir ./results
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[0]'

Flags

| Flag | Type | Default | Description |

|------|------|---------|-------------|

| NAME_OR_ID | string | required | Experiment name or ID (positional) |

| --dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |

| --space | string | none | Space name or ID (required if using dataset name instead of ID) |

| --all | bool | false | Use Arrow Flight for bulk export (see below) |

| --output-dir | string | . | Output directory |

| --stdout | bool | false | Print JSON to stdout instead of file |

| -p, --profile | string | default | Configuration profile |

REST vs Flight (--all)

  • REST (default): Lower friction -- no Arrow/Flight dependency, standard HTTPS ports, works through any corporate proxy or firewall. Limited to 500 runs per page.
  • Flight (--all): Required for experiments with more than 500 runs. Uses gRPC+TLS on a separate host/port (flight.arize.com:443) which some corporate networks may block.

Agent auto-escalation rule: If a REST export returns exactly 500 runs, the result is likely truncated. Re-run with --all to get the full dataset.

Output is a JSON array of run objects:

[
  {
    "id": "run_001",
    "example_id": "ex_001",
    "output": "The answer is 4.",
    "evaluations": {
      "correctness": { "label": "correct", "score": 1.0 },
      "relevance": { "score": 0.95, "explanation": "Directly answers the question" }
    },
    "metadata": { "model": "gpt-4o", "latency_ms": 1234 }
  }
]

Create Experiment: ax experiments create

Create a new experiment with runs from a data file.

ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
ax experiments create --name "claude-test" --dataset DATASET_NAME --space SPACE --file runs.csv

Flags

| Flag | Type | Required | Description |

|------|------|----------|-------------|

| --name, -n | string | yes | Experiment name |

| --dataset | string | yes | Dataset to run the experiment against |

| --space, -s | string | no | Space name or ID (required if using dataset name instead of ID) |

| --file, -f | path | yes | Data file with runs: CSV, JSON, JSONL, or Parquet |

| -o, --output | string | no | Output format |

| -p, --profile | string | no | Configuration profile |

Passing data via stdin

Use --file - to pipe data directly — no temp file needed:

echo '[{"example_id": "ex_001", "output": "Paris"}]' | ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file -

# Or with a heredoc
ax experiments create --name "my-experiment" --dataset DATASET_NAME --space SPACE --file - << 'EOF'
[{"example_id": "ex_001", "output": "Paris"}]
EOF

Required columns in the runs file

| Column | Type | Required | Description |

|--------|------|----------|-------------|

| example_id | string | yes | ID of the dataset example this run corresponds to |

| output | string | yes | The model/system output for this example |

Additional columns are passed through as additionalProperties on the run.

Delete Experiment: ax experiments delete

ax experiments delete NAME_OR_ID
ax experiments delete NAME_OR_ID --dataset DATASET_NAME --space SPACE   # required when using experiment name instead of ID
ax experiments delete NAME_OR_ID --force   # skip confirmation prompt

Flags

| Flag | Type | Default | Description |

|------|------|---------|-------------|

| NAME_OR_ID | string | required | Experiment name or ID (positional) |

| --dataset | string | none | Dataset name or ID (required if using experiment name instead of ID) |

| --space | string | none | Space name or ID (required if using dataset name instead of ID) |

| --force, -f | bool | false | Skip confirmation prompt |

| -p, --profile | string | default | Configuration profile |

Experiment Run Schema

Each run corresponds to one dataset example:

{
  "example_id": "required -- links to dataset example",
  "output": "required -- the model/system output for this example",
  "evaluations": {
    "metric_name": {
      "label": "optional string label (e.g., 'correct', 'incorrect')",
      "score": "optional numeric score (e.g., 0.95)",
      "explanation": "optional freeform text"
    }
  },
  "metadata": {
    "model": "gpt-4o",
    "temperature": 0.7,
    "latency_ms": 1234
  }
}

Evaluation fields

| Field | Type | Required | Description |

|-------|------|----------|-------------|

| label | string | no | Categorical classification (e.g., correct, incorrect, partial) |

| score | number | no | Numeric quality score (e.g., 0.0 - 1.0) |

| explanation | string | no | Freeform reasoning for the evaluation |

At least one of label, score, or explanation should be present per evaluation.

Workflows

Run an experiment against a dataset

  • Find or create a dataset:
   ax datasets list --space SPACE
   ax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'
  • Export the dataset examples:
   ax datasets export DATASET_NAME --space SPACE
  • Call the real model API for each example and collect outputs. Use ax datasets export --stdout to pipe examples directly into an inference script:
   ax datasets export DATASET_NAME --space SPACE --stdout | python3 infer.py > runs.json

Write infer.py to read examples from stdin, call the target model, and write runs JSON to stdout. The script below is a template — first inspect the exported dataset JSON to find the correct input field name, then uncomment the provider block the user wants:

   import json, sys, time

   examples = json.load(sys.stdin)
   runs = []

   for ex in examples:
       # Inspect the exported JSON to find the right field (e.g. "input", "question", "prompt")
       user_input = ex.get("input") or ex.get("question") or ex.get("prompt") or str(ex)

       start = time.time()

       # === CALL THE REAL MODEL API HERE — never fabricate or simulate ===
       # Uncomment and adapt the provider block the user requested:
       #
       # OpenAI (pip install openai  — uses OPENAI_API_KEY env var):
       #   from openai import OpenAI
       #   resp = OpenAI().chat.completions.create(
       #       model="gpt-4o",
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.choices[0].message.content
       #
       # Anthropic (pip install anthropic  — uses ANTHROPIC_API_KEY env var):
       #   import anthropic
       #   resp = anthropic.Anthropic().messages.create(
       #       model="claude-sonnet-4-6", max_tokens=1024,
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.content[0].text
       #
       # Google Gemini (pip install google-genai  — uses GOOGLE_API_KEY env var):
       #   from google import genai
       #   resp = genai.Client().models.generate_content(
       #       model="gemini-2.5-pro", contents=user_input
       #   )
       #   output_text = resp.text
       #
       # Custom / OpenAI-compatible proxy (pip install openai — uses CUSTOM_BASE_URL + CUSTOM_API_KEY env vars):
       # Use this for Azure OpenAI, NVIDIA NIM, local Ollama, or any OpenAI-compatible endpoint,
       # including a test integration proxy. Matches the `custom` provider in `ax ai-integrations create`.
       #   import os
       #   from openai import OpenAI
       #   resp = OpenAI(
       #       base_url=os.environ["CUSTOM_BASE_URL"],          # e.g. https://my-proxy.example.com/v1
       #       api_key=os.environ.get("CUSTOM_API_KEY", "none"),
       #   ).chat.completions.create(
       #       model=os.environ.get("CUSTOM_MODEL", "default"),
       #       messages=[{"role": "user", "content": user_input}]
       #   )
       #   output_text = resp.choices[0].message.content

       latency_ms = round((time.time() - start) * 1000)
       runs.append({
           "example_id": ex["id"],
           "output": output_text,
           "metadata": {"model": "MODEL_NAME", "latency_ms": latency_ms}
       })
       print(f"  {ex['id']}: {latency_ms}ms", file=sys.stderr)

   json.dump(runs, sys.stdout, indent=2)

Before running: install the provider SDK (pip install openai / anthropic / google-genai) and ensure the API key is set as an environment variable in your shell. If you cannot access the API, stop and tell the user what is needed.

  • Verify the runs file:
   python3 -c "import json; runs=json.load(open('runs.json')); print(f'{len(runs)} runs'); print(json.dumps(runs[0], indent=2))"

Each run must have example_id and output. Optional fields: evaluations, metadata.

  • Create the experiment:
   ax experiments create --name "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE --file runs.json
  • Verify: ax experiments get "gpt-4o-baseline" --dataset DATASET_NAME --space SPACE

Compare two experiments

  • Export both experiments:
   ax experiments export "experiment-a" --dataset DATASET_NAME --space SPACE --stdout > a.json
   ax experiments export "experiment-b" --dataset DATASET_NAME --space SPACE --stdout > b.json
  • Compare evaluation scores by example_id:
   # Average correctness score for experiment A
   jq '[.[] | .evaluations.correctness.score] | add / length' a.json

   # Same for experiment B
   jq '[.[] | .evaluations.correctness.score] | add / length' b.json
  • Find examples where results differ:
   jq -s '.[0] as $a | .[1][] | . as $run |
     {
       example_id: $run.example_id,
       b_score: $run.evaluations.correctness.score,
       a_score: ($a[] | select(.example_id == $run.example_id) | .evaluations.correctness.score)
     }' a.json b.json
  • Score distribution per evaluator (pass/fail/partial counts):
   # Count by label for experiment A
   jq '[.[] | .evaluations.correctness.label] | group_by(.) | map({label: .[0], count: length})' a.json
  • Find regressions (examples that passed in A but fail in B):
   jq -s '
     [.[0][] | select(.evaluations.correctness.label == "correct")] as $passed_a |
     [.[1][] | select(.evaluations.correctness.label != "correct") |
       select(.example_id as $id | $passed_a | any(.example_id == $id))
     ]
   ' a.json b.json

Statistical significance note: Score comparisons are most reliable with ≥ 30 examples per evaluator. With fewer examples, treat the delta as directional only — a 5% difference on n=10 may be noise. Report sample size alongside scores: jq 'length' a.json.

Download experiment results for analysis

  • ax experiments list --dataset DATASET_NAME --space SPACE -- find experiments
  • ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE -- download to file
  • Parse: jq '.[] | {example_id, score: .evaluations.correctness.score}' experiment_*/runs.json

Pipe export to other tools

# Count runs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq 'length'

# Extract all outputs
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '.[].output'

# Get runs with low scores
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq '[.[] | select(.evaluations.correctness.score < 0.5)]'

# Convert to CSV
ax experiments export EXPERIMENT_NAME --dataset DATASET_NAME --space SPACE --stdout | jq -r '.[] | [.example_id, .output, .evaluations.correctness.score] | @csv'

Related Skills

  • arize-dataset: Create or export the dataset this experiment runs against → use arize-dataset first
  • arize-prompt-optimization: Use experiment results to improve prompts → next step is arize-prompt-optimization
  • arize-trace: Inspect individual span traces for failing experiment runs → use arize-trace
  • arize-link: Generate clickable UI links to traces from experiment runs → use arize-link

Troubleshooting

| Problem | Solution |

|---------|----------|

| ax: command not found | See references/ax-setup.md |

| 401 Unauthorized | API key is wrong, expired, or doesn't have access to this space. Fix the profile using references/ax-profiles.md. |

| No profile found | No profile is configured. See references/ax-profiles.md to create one. |

| Experiment not found | Verify experiment name with ax experiments list --space SPACE |

| Invalid runs file | Each run must have example_id and output fields |

| example_id mismatch | Ensure example_id values match IDs from the dataset (export dataset to verify) |

| No runs found | Export returned empty -- verify experiment has runs via ax experiments get |

| Dataset not found | The linked dataset may have been deleted; check with ax datasets list |

Save Credentials for Future Use

See references/ax-profiles.md § Save Credentials for Future Use.

How to use it

Copy the folder

Take github/arize-experiment from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference pip. Without those the skill loads but fails at the first command.