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Claude Agent Skill · by Github

Arize Experiment

Install Arize Experiment skill for Claude Code from github/awesome-copilot.

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Terminal · npx
$npx skills add https://github.com/microsoft/github-copilot-for-azure --skill azure-ai
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How Arize Experiment fits into a Paperclip company.

Arize Experiment drops into any Paperclip agent that handles this kind of work. Assign it to a specialist inside a pre-configured PaperclipOrg company and the skill becomes available on every heartbeat — no prompt engineering, no tool wiring.

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SKILL.md326 lines
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---name: arize-experimentdescription: "INVOKE THIS SKILL when creating, running, or analyzing Arize experiments. Covers experiment CRUD, exporting runs, comparing results, and evaluation workflows using the ax CLI."--- # Arize Experiment Skill ## 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: check `.env` for `ARIZE_API_KEY` and use it to create/update the profile via references/ax-profiles.md. If `.env` has no key either, ask the user for their Arize API key (https://app.arize.com/admin > API Keys)- Space ID unknown → check `.env` for `ARIZE_SPACE_ID`, or run `ax spaces list -o json`, or ask the user- Project unclear → check `.env` for `ARIZE_DEFAULT_PROJECT`, or ask, or run `ax projects list -o json --limit 100` and present as selectable options ## List Experiments: `ax experiments list` Browse experiments, optionally filtered by dataset. Output goes to stdout. ```bashax experiments listax experiments list --dataset-id DATASET_ID --limit 20ax experiments list --cursor CURSOR_TOKENax experiments list -o json``` ### Flags | Flag | Type | Default | Description ||------|------|---------|-------------|| `--dataset-id` | 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. ```bashax experiments get EXPERIMENT_IDax experiments get EXPERIMENT_ID -o json``` ### Flags | Flag | Type | Default | Description ||------|------|---------|-------------|| `EXPERIMENT_ID` | string | required | Positional argument || `-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. ```bashax experiments export EXPERIMENT_ID# -> experiment_abc123_20260305_141500/runs.json ax experiments export EXPERIMENT_ID --allax experiments export EXPERIMENT_ID --output-dir ./resultsax experiments export EXPERIMENT_ID --stdoutax experiments export EXPERIMENT_ID --stdout | jq '.[0]'``` ### Flags | Flag | Type | Default | Description ||------|------|---------|-------------|| `EXPERIMENT_ID` | string | required | Positional argument || `--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: ```json[  {    "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. ```bashax experiments create --name "gpt-4o-baseline" --dataset-id DATASET_ID --file runs.jsonax experiments create --name "claude-test" --dataset-id DATASET_ID --file runs.csv``` ### Flags | Flag | Type | Required | Description ||------|------|----------|-------------|| `--name, -n` | string | yes | Experiment name || `--dataset-id` | string | yes | Dataset to run the experiment against || `--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: ```bashecho '[{"example_id": "ex_001", "output": "Paris"}]' | ax experiments create --name "my-experiment" --dataset-id DATASET_ID --file - # Or with a heredocax experiments create --name "my-experiment" --dataset-id DATASET_ID --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` ```bashax experiments delete EXPERIMENT_IDax experiments delete EXPERIMENT_ID --force   # skip confirmation prompt``` ### Flags | Flag | Type | Default | Description ||------|------|---------|-------------|| `EXPERIMENT_ID` | string | required | Positional argument || `--force, -f` | bool | false | Skip confirmation prompt || `-p, --profile` | string | default | Configuration profile | ## Experiment Run Schema Each run corresponds to one dataset example: ```json{  "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 1. Find or create a dataset:   ```bash   ax datasets list   ax datasets export DATASET_ID --stdout | jq 'length'   ```2. Export the dataset examples:   ```bash   ax datasets export DATASET_ID   ```3. Process each example through your system, collecting outputs and evaluations4. Build a runs file (JSON array) with `example_id`, `output`, and optional `evaluations`:   ```json   [     {"example_id": "ex_001", "output": "4", "evaluations": {"correctness": {"label": "correct", "score": 1.0}}},     {"example_id": "ex_002", "output": "Paris", "evaluations": {"correctness": {"label": "correct", "score": 1.0}}}   ]   ```5. Create the experiment:   ```bash   ax experiments create --name "gpt-4o-baseline" --dataset-id DATASET_ID --file runs.json   ```6. Verify: `ax experiments get EXPERIMENT_ID` ### Compare two experiments 1. Export both experiments:   ```bash   ax experiments export EXPERIMENT_ID_A --stdout > a.json   ax experiments export EXPERIMENT_ID_B --stdout > b.json   ```2. Compare evaluation scores by `example_id`:   ```bash   # 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   ```3. Find examples where results differ:   ```bash   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   ```4. Score distribution per evaluator (pass/fail/partial counts):   ```bash   # Count by label for experiment A   jq '[.[] | .evaluations.correctness.label] | group_by(.) | map({label: .[0], count: length})' a.json   ```5. Find regressions (examples that passed in A but fail in B):   ```bash   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 1. `ax experiments list --dataset-id DATASET_ID` -- find experiments2. `ax experiments export EXPERIMENT_ID` -- download to file3. Parse: `jq '.[] | {example_id, score: .evaluations.correctness.score}' experiment_*/runs.json` ### Pipe export to other tools ```bash# Count runsax experiments export EXPERIMENT_ID --stdout | jq 'length' # Extract all outputsax experiments export EXPERIMENT_ID --stdout | jq '.[].output' # Get runs with low scoresax experiments export EXPERIMENT_ID --stdout | jq '[.[] | select(.evaluations.correctness.score < 0.5)]' # Convert to CSVax experiments export EXPERIMENT_ID --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 ID with `ax experiments list` || `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.