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Arize Dataset

  • 1k installs
  • 37.1k repo stars
  • Updated July 28, 2026
  • github/awesome-copilot

This is a copy of arize-dataset by arize-ai - installs and ranking accrue to the original listing.

arize-dataset is a Claude Code skill that repairs Arize ax CLI authentication and region profiles for developers who upload or manage evaluation datasets without 401 failures.

About

arize-dataset is an Arize ax CLI setup skill consulted when authentication fails with 401 errors, missing profiles, or unset API keys blocking dataset work. The workflow starts with ax profiles show to inspect API key, profile, endpoint, and region state, then walks through creating or updating profiles when keys are wrong, expired, or pointed at the wrong region. Developers reach for arize-dataset only after failures appear—not for proactive checks—when evaluation datasets cannot upload or sync to Arize. The skill targets the gap between local ax configuration and successful dataset management so LLM evaluation pipelines can resume without manual dashboard guesswork.

  • Diagnostic flow: `ax profiles show` before changing keys or regions
  • Patch misconfigured profiles with `ax profiles update` without wiping other fields
  • Requires API keys via ARIZE_API_KEY env var—never pass raw keys on the CLI
  • Region correction example: us-east-1b via update flags
  • Consult-only on auth failure—not meant as a proactive health check ritual

Arize Dataset by the numbers

  • 1,021 all-time installs (skills.sh)
  • +24 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
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Installs1k
repo stars37.1k
Security audit2 / 3 scanners passed
Last updatedJuly 28, 2026
Repositorygithub/awesome-copilot

How do you fix Arize ax CLI 401 authentication errors?

Fix Arize `ax` CLI authentication and region profiles so you can upload or manage evaluation datasets without 401 failures.

Who is it for?

ML engineers hitting 401 Unauthorized or missing-profile errors while uploading Arize evaluation datasets via ax CLI.

Skip if: Developers who need general Arize dashboard analytics without ax CLI dataset upload failures.

When should I use this skill?

ax CLI returns 401, reports no profiles, shows a missing API key, or dataset upload commands fail authentication.

What you get

Working ax CLI profile with valid API key, correct region, and successful dataset upload commands.

  • configured ax profile
  • successful dataset upload

Files

SKILL.mdMarkdownGitHub ↗

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

FlagTypeDefaultDescription
--spacestringfrom profileFilter by space
--limit, -lint15Max results (1-100)
--cursorstringnonePagination cursor from previous response
-o, --outputstringtableOutput format: table, json, csv, parquet, or file path
-p, --profilestringdefaultConfiguration 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

FlagTypeDefaultDescription
NAME_OR_IDstringrequiredDataset name or ID (positional)
--spacestringnoneSpace name or ID (required if using dataset name instead of ID)
-o, --outputstringtableOutput format
-p, --profilestringdefaultConfiguration profile

Response fields

FieldTypeDescription
idstringDataset ID
namestringDataset name
space_idstringSpace this dataset belongs to
created_atdatetimeWhen the dataset was created
updated_atdatetimeLast modification time
versionsarrayList 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

FlagTypeDefaultDescription
NAME_OR_IDstringrequiredDataset name or ID (positional)
--spacestringnoneSpace name or ID (required if using dataset name instead of ID)
--version-idstringlatestExport a specific dataset version
--allboolfalseUnlimited bulk export (use for datasets > 500 examples)
--output-dirstring.Output directory
--stdoutboolfalsePrint JSON to stdout instead of file
-p, --profilestringdefaultConfiguration 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

FlagTypeRequiredDescription
--name, -nstringyesDataset name
--spacestringyesSpace to create the dataset in
--file, -fpathyesData file: CSV, JSON, JSONL, or Parquet
-o, --outputstringnoOutput format for the returned dataset metadata
-p, --profilestringnoConfiguration 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

FormatExtensionNotes
CSV.csvColumn headers become field names
JSON.jsonArray of objects
JSON Lines.jsonlOne object per line (NOT a JSON array)
Parquet.parquetColumn 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

FlagTypeRequiredDescription
NAME_OR_IDstringyesDataset name or ID (positional); add --space when using name
--spacestringnoSpace name or ID (required if using dataset name instead of ID)
--jsonstringmutexJSON array of example objects
--file, -fpathmutexData file (CSV, JSON, JSONL, Parquet)
--version-idstringnoAppend to a specific version (default: latest)
-o, --outputstringnoOutput format for the returned dataset metadata
-p, --profilestringnoConfiguration 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

FlagTypeDefaultDescription
NAME_OR_IDstringrequiredDataset name or ID (positional)
--spacestringnoneSpace name or ID (required if using dataset name instead of ID)
--force, -fboolfalseSkip confirmation prompt
-p, --profilestringdefaultConfiguration 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:

FieldTypeManaged byNotes
idstringserverAuto-generated UUID. Required on update, forbidden on create/append
created_atdatetimeserverImmutable creation timestamp
updated_atdatetimeserverAuto-updated on modification
(any user field)any JSON typeuserString, 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

ProblemSolution
ax: command not foundSee references/ax-setup.md
401 UnauthorizedAPI key is wrong, expired, or doesn't have access to this space. Fix the profile using references/ax-profiles.md.
No profile foundNo profile is configured. See references/ax-profiles.md to create one.
Dataset not foundVerify dataset ID with ax datasets list
File format errorSupported: CSV, JSON, JSONL, Parquet. Use --file - to read from stdin.
platform-managed columnRemove id, created_at, updated_at from create/append payloads
reserved columnRemove time, count, or any source_record_* field
Provide either --json or --fileAppend requires exactly one input source
Examples array is emptyEnsure your JSON array or file contains at least one example
not a JSON objectEach 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.

Related skills

FAQ

When should I invoke arize-dataset?

arize-dataset runs when Arize ax CLI authentication fails—401 errors, missing profiles, unset API keys, or wrong region settings—blocking evaluation dataset uploads. The skill is not meant for proactive profile checks.

What command does arize-dataset inspect first?

arize-dataset starts with ax profiles show to read API key, profile, endpoint, and region state, then guides fixes when keys are missing, expired, or misconfigured for dataset management.

Is Arize Dataset safe to install?

skills.sh reports 2 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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