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arize-annotation

Automate annotation configuration and queue management in Arize via CLI and Python SDK.

5|6|Updated Jun 28, 2026
Search Tags:#human-in-the-loop#annotation#data labeling#annotation queue#Arize
One-click install
npx skills add https://github.com/seldo/aiewf-2026-demo --skill arize-annotation-seldo
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: arize-annotation
Source: https://github.com/seldo/aiewf-2026-demo/tree/main/.agents/skills/arize-annotation
Command: npx skills add https://github.com/seldo/aiewf-2026-demo --skill arize-annotation-seldo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires arize-ax-cli, and includes scripts (resource) and references (resource) components.

What problem does it solve?

The Arize Annotation Skill addresses the need for efficient management and application of human feedback on Arize, streamlining the process of creating and updating annotation configurations and queues.

Core Features & Use Cases

  • Annotation Configurations: Define and manage categorical, continuous, and freeform label schemas for various data types.
  • Annotation Queues: Create and manage human review workflows to route records for labeling by human annotators.
  • Python SDK Integration: Apply annotations to project spans programmatically using the Python SDK.
  • Use Case: For a data science team using Arize, this skill helps in setting up a labeling queue for new model features, ensuring high-quality data for training and validation.

Quick Start

Use the arize-annotation skill to create a new annotation queue for model evaluation with the command: arize-annotation create queue --name "Model Evaluation Queue" --config-id <CONFIG_ID>.

Frequently Asked Questions about arize-annotation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create an annotation queue in Arize for human review?▼

To create an annotation queue in Arize, use the command `arize-annotation create queue` with parameters for the queue name and configuration ID. This automates routing records to human annotators for model evaluation labeling.

What types of annotation label schemas can I configure on the Arize platform?▼

Arize annotation configurations support categorical, continuous, and freeform label schemas for various data types. You can define these schemas to structure human feedback for your specific data science project requirements.

Do I need the arize-ax CLI to manage annotation configurations?▼

Yes, the arize-ax CLI is a required dependency for the arize-annotation skill. It provides the necessary interface to interact with the Arize platform to create, manage, and update annotation queues and configurations.

Can I apply annotations to project spans programmatically without manual labeling?▼

Yes, you can apply annotations programmatically using the Python SDK integration. This allows you to automatically label project spans based on your defined configuration schemas, bypassing the manual human review queue when needed.

What is the best way to manage human-in-the-loop workflows for model evaluation on Arize?▼

The best way to manage human-in-the-loop workflows on Arize is by defining annotation configurations and creating dedicated annotation queues. This streamlines routing records to human annotators, ensuring high-quality training and validation data.

Are there limitations when updating existing annotation queues in Arize?▼

Updating annotation queues in Arize requires the arize-ax CLI and proper configuration IDs. You must ensure your queue configurations match your data schemas before routing records to avoid misaligned human feedback during the labeling process.

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