MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Arize-Dataset

Arize-Dataset is an data AI skill with a core value of Creates, manages, and queries Arize datasets and examples. It helps developers solve real-world problems in the data domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

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,

Last verified on: 2026-07-14

Quick Facts

Category data
Works With Claude, GitHub Copilot
Source github/awesome-copilot
Stars ⭐ 34.1k
Last Verified 2026-07-14
Risk Level Low
mkdir -p ./skills/arize-dataset && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/arize-dataset/SKILL.md -o ./skills/arize-dataset/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# 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.


bash
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.


bash
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).


bash
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 --

🎯 Best For

  • QA engineers
  • Developers writing unit tests
  • Claude users
  • GitHub Copilot users
  • Data professionals

💡 Use Cases

  • Generating test cases for edge conditions
  • Writing integration test suites
  • Data pipeline auditing
  • Query optimization

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Arize-Dataset to Your Work

    Provide context for your task — paste source material, describe your audience, or share existing work to guide the AI.

  4. 4

    Review and Refine

    Edit the AI output for accuracy, tone, and completeness. Add human insight where the AI lacks context.

❓ Frequently Asked Questions

Does this generate test mocks?

Many testing skills include mock generation. Check the install command and skill content for details.

How do I install Arize-Dataset?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/arize-dataset/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Not testing edge cases

AI tends to generate happy-path tests. Manually review for boundary conditions.

Ignoring data quality

AI analysis inherits all data quality issues — profile your data first.

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