Skill

github/awesome-copilot28.7k

arize-dataset

"INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI."

$npx clawhub@latest install arize-dataset

GitHub

Overview

# Arize Dataset Skill

Concepts

**Dataset** = a versioned collection of examples used for evaluation and experimentation
**Dataset Version** = a snapshot of a dataset at a point in time; updates can be in-place or create a new version
**Example** = a single record in a dataset with arbitrary user-defined fields (e.g., `question`, `answer`, `context`)
**Space** = an organizational container; datasets belong to a space

System-managed fields on examples (`id`, `created_at`, `updated_at`) are auto-generated by the server -- never include them in create or append payloads.

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 Datasets: `ax datasets list`

Browse datasets in a space. Output goes to stdout.

```bash

ax datasets list

ax datasets list --space-id SPACE_ID --limit 20

ax datasets list --cursor CURSOR_TOKEN

ax datasets list -o json

```

Flags

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

| `--limit, -l` | int | 15 | Max results (1-100) |

Get Dataset: `ax datasets get`

Quick metadata lookup -- returns dataset name, space, timestamps, and version list.

```bash

ax datasets get DATASET_ID

ax datasets get DATASET_ID -o json

```

Flags

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

Response fields

| Field | Type | Description |

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

| `id` | string | Dataset ID |

| `name` | string | Dataset name |

| `space_id` | string | Space this dataset belongs to |

| `created_at` | datetime | When the dataset was created |

| `updated_at` | datetime | Last modification time |

| `versions` | array | List of dataset versions (id, name, dataset_id, created_at, updated_at) |

Export Dataset: `ax datasets export`

Download all examples to a file. Use `--all` for datasets larger than 500 examples (unlimited bulk export).

```bash

ax datasets export DATASET_ID

# -> dataset_abc123_20260305_141500/examples.json

ax datasets export DATASET_ID --all

ax datasets export DATASET_ID --version-id VERSION_ID

ax datasets export DATASET_ID --output-dir ./data

ax datasets export DATASET_ID --stdout

ax datasets export DATASET_ID --stdout | jq '.[0]'

```

Flags

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

**Agent auto-escalation rule:** If an export returns exactly 500 examples, the result is likely truncated — re-run with `--all` to get the full dataset.

**Export completeness verification:** After exporting, confirm the row count matches what the server reports:

```bash

# Get the server-reported count from dataset metadata

ax datasets get DATASET_ID -o json | jq '.versions[-1] | {version: .id, examples: .example_count}'

# Compare to what was exported

jq 'length' dataset_*/examples.json

# If counts differ, re-export with --all

```

Output is a JSON array of example objects. Each example has system fields (`id`, `created_at`, `updated_at`) plus all user-defined fields:

```json

[

{

"id": "ex_001",

"created_at": "2026-01-15T10:00:00Z",

"updated_at": "2026-01-15T10:00:00Z",

"question": "What is 2+2?",

"answer": "4",

"topic": "math"

}

]

```

Create Dataset: `ax datasets create`

Create a new dataset from a data file.

```bash

ax datasets create --name "My Dataset" --space-id SPACE_ID --file data.csv

ax datasets create --name "My Dataset" --space-id SPACE_ID --file data.json

ax datasets create --name "My Dataset" --space-id SPACE_ID --file data.jsonl

ax datasets create --name "My Dataset" --space-id SPACE_ID --file data.parquet

```

Flags

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

Passing data via stdin

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

```bash

echo '[{"question": "What is 2+2?", "answer": "4"}]' | ax datasets create --name "my-dataset" --space-id SPACE_ID --file -

# Or with a heredoc

ax datasets create --name "my-dataset" --space-id SPACE_ID --file - << 'EOF'

[{"question": "What is 2+2?", "answer": "4"}]

EOF

```

To add rows to an existing dataset, use `ax datasets append --json '[...]'` instead — no file needed.

Supported file formats

| Format | Extension | Notes |

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

| CSV | `.csv` | Column headers become field names |

| JSON | `.json` | Array of objects |

| JSON Lines | `.jsonl` | One object per line (NOT a JSON array) |

| Parquet | `.parquet` | Column names become field names; preserves types |

**Format gotchas:**

**CSV**: Loses type information — dates become strings, `null` becomes empty string. Use JSON/Parquet to preserve types.
**JSONL**: Each line is a separate JSON object. A JSON array (`[{...}, {...}]`) in a `.jsonl` file will fail — use `.json` extension instead.
**Parquet**: Preserves column types. Requires `pandas`/`pyarrow` to read locally: `pd.read_parquet("examples.parquet")`.

Append Examples: `ax datasets append`

Add examples to an existing dataset. Two input modes -- use whichever fits.

Inline JSON (agent-friendly)

Generate the payload directly -- no temp files needed:

```bash

ax datasets append DATASET_ID --json '[{"question": "What is 2+2?", "answer": "4"}]'

ax datasets append DATASET_ID --json '[

{"question": "What is gravity?", "answer": "A fundamental force..."},

{"question": "What is light?", "answer": "Electromagnetic radiation..."}

]'

```

From a file

```bash

ax datasets append DATASET_ID --file new_examples.csv

ax datasets append DATASET_ID --file additions.json

```

To a specific version

```bash

ax datasets append DATASET_ID --json '[{"q": "..."}]' --version-id VERSION_ID

```

Flags

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

Exactly one of `--json` or `--file` is required.

Validation

Each example must be a JSON object with at least one user-defined field
Maximum 100,000 examples per request

**Schema validation before append:** If the dataset already has examples, inspect its schema before appending to avoid silent field mismatches:

```bash

# Check existing field names in the dataset

ax datasets export DATASET_ID --stdout | jq '.[0] | keys'

# Verify your new data has matching field names

echo '[{"question": "..."}]' | jq '.[0] | keys'

# Both outputs should show the same user-defined fields

```

Fields are free-form: extra fields in new examples are added, and missing fields become null. However, typos in field names (e.g., `queston` vs `question`) create new columns silently -- verify spelling before appending.

Delete Dataset: `ax datasets delete`

```bash

ax datasets delete DATASET_ID

ax datasets delete DATASET_ID --force # skip confirmation prompt

```

Flags

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

Workflows

Find a dataset by name

Users often refer to datasets by name rather than ID. Resolve a name to an ID before running other commands:

```bash

# Find dataset ID by name

ax datasets list -o json | jq '.[] | select(.name == "eval-set-v1") | .id'

# If the list is paginated, fetch more

ax datasets list -o json --limit 100 | jq '.[] | select(.name | test("eval-set")) | {id, name}'

```

Create a dataset from file for evaluation

1. Prepare a CSV/JSON/Parquet file with your evaluation columns (e.g., `input`, `expected_output`)

If generating data inline, pipe it via stdin using `--file -` (see the Create Dataset section)

2. `ax datasets create --name "eval-set-v1" --space-id SPACE_ID --file eval_data.csv`

3. Verify: `ax datasets get DATASET_ID`

4. Use the dataset ID to run experiments

Add examples to an existing dataset

```bash

# Find the dataset

ax datasets list

# Append inline or from a file (see Append Examples section for full syntax)

ax datasets append DATASET_ID --json '[{"question": "...", "answer": "..."}]'

ax datasets append DATASET_ID --file additional_examples.csv

```

Download dataset for offline analysis

1. `ax datasets list` -- find the dataset

2. `ax datasets export DATASET_ID` -- download to file

3. Parse the JSON: `jq '.[] | .question' dataset_*/examples.json`

Export a specific version

```bash

# List versions

ax datasets get DATASET_ID -o json | jq '.versions'

# Export that version

ax datasets export DATASET_ID --version-id VERSION_ID

```

Iterate on a dataset

1. Export current version: `ax datasets export DATASET_ID`

2. Modify the examples locally

3. Append new rows: `ax datasets append DATASET_ID --file new_rows.csv`

4. Or create a fresh version: `ax datasets create --name "eval-set-v2" --space-id SPACE_ID --file updated_data.json`

Pipe export to other tools

```bash

# Count examples

ax datasets export DATASET_ID --stdout | jq 'length'

# Extract a single field

ax datasets export DATASET_ID --stdout | jq '.[].question'

# Convert to CSV with jq

ax datasets export DATASET_ID --stdout | jq -r '.[] | [.question, .answer] | @csv'

```

Dataset Example Schema

Examples are free-form JSON objects. There is no fixed schema -- columns are whatever fields you provide. System-managed fields are added by the server:

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

**arize-trace**: Export production spans to understand what data to put in datasets → use `arize-trace`
**arize-experiment**: Run evaluations against this dataset → next step is `arize-experiment`
**arize-prompt-optimization**: Use dataset + experiment results to improve prompts → use `arize-prompt-optimization`

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. |

| `Dataset not found` | Verify dataset ID with `ax datasets list` |

| `File format error` | Supported: CSV, JSON, JSONL, Parquet. Use `--file -` to read from stdin. |

| `platform-managed column` | Remove `id`, `created_at`, `updated_at` from create/append payloads |

| `reserved column` | Remove `time`, `count`, or any `source_record_*` field |

| `Provide either --json or --file` | Append requires exactly one input source |

| `Examples array is empty` | Ensure your JSON array or file contains at least one example |

| `not a JSON object` | Each element in the `--json` array must be a `{...}` object, not a string or number |

Save Credentials for Future Use

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

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