awesome-copilot/plugins/arize-ax/skills/arize-dataset/SKILL.md

name, description

name	description
arize-dataset	INVOKE THIS SKILL when creating, managing, or querying Arize datasets and examples. Also use when the user needs test data or evaluation examples for their model. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI.

Arize Dataset 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

• 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, 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.

List Datasets: ax datasets list

Browse datasets in a space. Output goes to stdout.

ax datasets list
ax datasets list --space SPACE --limit 20
ax datasets list --cursor CURSOR_TOKEN
ax datasets list -o json

Flags

Flag	Type	Default	Description
--space	string	from profile	Filter by space
--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 Dataset: ax datasets get

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

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

Flags

Flag	Type	Default	Description
NAME_OR_ID	string	required	Dataset name or ID (positional)
--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	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).

ax datasets export NAME_OR_ID
# -> dataset_abc123_20260305_141500/examples.json

ax datasets export NAME_OR_ID --all
ax datasets export NAME_OR_ID --version-id VERSION_ID
ax datasets export NAME_OR_ID --output-dir ./data
ax datasets export NAME_OR_ID --stdout
ax datasets export NAME_OR_ID --stdout | jq '.[0]'
ax datasets export NAME_OR_ID --space SPACE   # required when using dataset name instead of ID

Flags

Flag	Type	Default	Description
NAME_OR_ID	string	required	Dataset name or ID (positional)
--space	string	none	Space name or ID (required if using dataset name instead of ID)
--version-id	string	latest	Export a specific dataset version
--all	bool	false	Unlimited bulk export (use for datasets > 500 examples)
--output-dir	string	.	Output directory
--stdout	bool	false	Print JSON to stdout instead of file
-p, --profile	string	default	Configuration profile

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:

# Get the server-reported count from dataset metadata
ax datasets get DATASET_NAME --space SPACE -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:

[
  {
    "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.

ax datasets create --name "My Dataset" --space SPACE --file data.csv
ax datasets create --name "My Dataset" --space SPACE --file data.json
ax datasets create --name "My Dataset" --space SPACE --file data.jsonl
ax datasets create --name "My Dataset" --space SPACE --file data.parquet

Flags

Flag	Type	Required	Description
--name, -n	string	yes	Dataset name
--space	string	yes	Space to create the dataset in
--file, -f	path	yes	Data file: CSV, JSON, JSONL, or Parquet
-o, --output	string	no	Output format for the returned dataset metadata
-p, --profile	string	no	Configuration profile

Passing data via stdin

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

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

# Or with a heredoc
ax datasets create --name "my-dataset" --space SPACE --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:

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

ax datasets append DATASET_NAME --space SPACE --json '[
  {"question": "What is gravity?", "answer": "A fundamental force..."},
  {"question": "What is light?", "answer": "Electromagnetic radiation..."}
]'

From a file

ax datasets append DATASET_NAME --space SPACE --file new_examples.csv
ax datasets append DATASET_NAME --space SPACE --file additions.json

To a specific version

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

Flags

Flag	Type	Required	Description
NAME_OR_ID	string	yes	Dataset name or ID (positional); add --space when using name
--space	string	no	Space name or ID (required if using dataset name instead of ID)
--json	string	mutex	JSON array of example objects
--file, -f	path	mutex	Data file (CSV, JSON, JSONL, Parquet)
--version-id	string	no	Append to a specific version (default: latest)
-o, --output	string	no	Output format for the returned dataset metadata
-p, --profile	string	no	Configuration profile

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:

# Check existing field names in the dataset
ax datasets export DATASET_NAME --space SPACE --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

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

Flags

Flag	Type	Default	Description
NAME_OR_ID	string	required	Dataset name or ID (positional)
--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

Workflows

Find a dataset by name

All dataset commands accept a name or ID directly. You can pass a dataset name as the positional argument (add --space SPACE when not using an ID):

# Use name directly
ax datasets get "eval-set-v1" --space SPACE
ax datasets export "eval-set-v1" --space SPACE

# Or resolve name to ID via list if you need the base64 ID
ax datasets list -o json | jq '.[] | select(.name == "eval-set-v1") | .id'

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 SPACE --file eval_data.csv
3. Verify: ax datasets get DATASET_NAME --space SPACE
4. Use the dataset name to run experiments

Add examples to an existing dataset

# Find the dataset
ax datasets list --space SPACE

# Append inline or from a file using the dataset name (see Append Examples section for full syntax)
ax datasets append DATASET_NAME --space SPACE --json '[{"question": "...", "answer": "..."}]'
ax datasets append DATASET_NAME --space SPACE --file additional_examples.csv

Download dataset for offline analysis

1. ax datasets list --space SPACE -- find the dataset name
2. ax datasets export DATASET_NAME --space SPACE -- download to file
3. Parse the JSON: jq '.[] | .question' dataset_*/examples.json

Export a specific version

# List versions
ax datasets get DATASET_NAME --space SPACE -o json | jq '.versions'

# Export that version
ax datasets export DATASET_NAME --space SPACE --version-id VERSION_ID

Iterate on a dataset

1. Export current version: ax datasets export DATASET_NAME --space SPACE
2. Modify the examples locally
3. Append new rows: ax datasets append DATASET_NAME --space SPACE --file new_rows.csv
4. Or create a fresh version: ax datasets create --name "eval-set-v2" --space SPACE --file updated_data.json

Pipe export to other tools

# Count examples
ax datasets export DATASET_NAME --space SPACE --stdout | jq 'length'

# Extract a single field
ax datasets export DATASET_NAME --space SPACE --stdout | jq '.[].question'

# Convert to CSV with jq
ax datasets export DATASET_NAME --space SPACE --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:

Field	Type	Managed by	Notes
id	string	server	Auto-generated UUID. Required on update, forbidden on create/append
created_at	datetime	server	Immutable creation timestamp
updated_at	datetime	server	Auto-updated on modification
(any user field)	any JSON type	user	String, number, boolean, null, nested object, array

Related Skills

• 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.