Arize Dataset

What it does

When your agent uses it

• The user needs test data
• Evaluation examples
• Mentions create dataset
• Append examples

Example prompts

• “/arize-dataset”

Requirements

• A credential in CURSOR_TOKEN
• Compatibility (from SKILL.md): Requires the ax CLI and a configured Arize profile.

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

1. 1Prepare a CSV/JSON/Parquet file with your evaluation columns (e.g., input, expected_output)
2. 2ax datasets create --name "eval-set-v1" --space SPACE --file eval_data.csv
3. 3Verify: ax datasets get DATASET_NAME --space SPACE
4. 4Use the dataset name to run experiments

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Shell commands in SKILL.md call:

  • jq

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • CURSOR_TOKEN

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires the ax CLI and a configured Arize profile.

  From compatibility in the SKILL.md frontmatter.

Context cost

Arize Dataset loads about 3.9k tokens when it runs, and up to ~5.4k if it reads all its reference files. Until then it costs about 89 tokens; SKILL.md has 1,408 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~89

When it runs · the whole SKILL.md, loaded when a task matches

~3.9k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~5.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NoteMentions a .env fileSKILL.md:32

  - **Security:** Never read `.env` files or search the filesystem for credentials. Use `ax profiles` for Arize credential

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 1,408 words, ~3,854 tokens.

name

arize-dataset

description

Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.

compatibility

Requires the ax CLI and a configured Arize profile.

metadata.author

arize

metadata.version

1.0

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

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

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

# 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

[
  {
    "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"
  }
]

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

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

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..."}
]'

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

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

# 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

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

# 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'

# 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

# 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

# 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'

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/arize-dataset of github/awesome-copilot.

• SKILL.md
• references/ax-profiles.md
• references/ax-setup.md

Open the folder on GitHubat commit 727ff2e

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

• boshi-xixixi/TraeSkill.trae/Skills/.agents/skills/arize-dataset