Arize Annotation

If you're running human review loops on LLM outputs in Arize, this handles the setup work. You define label schemas (categorical like correct/incorrect, continuous scores, or freeform text) and create queues that route spans or dataset examples to reviewers. The CLI covers the full CRUD cycle for both configs and queues, plus you can bulk annotate spans through the Python SDK. It's the bridge between your traces and the people who need to label them. The docs are thorough on the schema options and queue assignment logic, which matters when you're coordinating multiple reviewers across different label types.

skills.sh installs: 3.1K
GitHub stars: 72
last updated: Aug 21, 2026

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Install

$ npx -y skills add arize-ai/arize-skills --skill arize-annotation --agent claude-code

Installs into .claude/skills of the current project.

Files

Arize Annotation Skill

SPACE — --space flags accept a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Resolve a known name with ax spaces get NAME; ax spaces list is for human browsing.

This skill covers annotation configs (the label schema) and annotation queues (human review workflows), as well as programmatically annotating project spans via the Python SDK.

Direction: Human labeling in Arize attaches values defined by configs to spans, dataset examples, experiment-related records, and queue items in the product UI. This skill covers: ax annotation-configs, ax annotation-queues, and bulk span updates with ArizeClient.spans.update_annotations.