arize-annotation
Helps your agent create and manage annotation configurations and human review workflows. It allows applying human annotations to data to improve quality.
What it does
This tool helps organize and manage how people evaluate data on a platform called Arize. You can create 'annotation configs' to define the types of evaluations (like choosing from options, giving scores, or writing comments) and 'annotation queues' to organize who performs these evaluations.
When to use it
- ✓When you want to define how people will provide feedback on data.
- ✓When you need to organize a group of people to review and evaluate data.
- ✓When you want to apply human-made evaluations to your data.
Example of what to ask your AI
“Create an annotation config to evaluate response quality, allowing scores from 0 to 10.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
Internally, the tool uses commands to create and manage these configs and queues. It allows defining question types (choice, score, free text) and who will respond, as well as applying these responses to the data.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill arize-annotationOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/arize-annotation/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
Arize Annotation Skill
SPACE— All--spaceflags and theARIZE_SPACEenv var accept a space name (e.g.,my-workspace) or a base64 space ID (e.g.,U3BhY2U6...). Find yours withax spaces list.
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.
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 foundor version error → see references/ax-setup.md401 Unauthorized/ missing API key → runax profiles showto 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 listto pick by name, or ask the user - Security: Never read
.envfiles or search the filesystem for credentials. Useax profilesfor Arize credentials andax ai-integrationsfor LLM provider keys. If credentials are not available through these channels, ask the user.
Concepts
What is an Annotation Config?
An annotation config defines the schema for a single type of human feedback label. Before anyone can annotate a span, dataset record, experiment output, or queue item, a config must exist for that label in the space.
| Field | Description |
|---|---|
| Name | Descriptive identifier (e.g. Correctness, Helpfulness). Must be unique within the space. |
| Type | categorical (pick from a list), continuous (numeric range), or freeform (free text). |
| Values | For categorical: array of {"label": str, "score": number} pairs. |
| Min/Max Score | For continuous: numeric bounds. |
| Optimization Direction | Whether higher scores are better (maximize) or worse (minimize). Used to render trends in the UI. |
Where labels get applied (surfaces)
| Surface | Typical path |
|---|---|
| Project spans | Python SDK spans.update_annotations (below) and/or the Arize UI |
| Dataset examples | Arize UI (human labeling flows); configs must exist in the space |
| Experiment outputs | Often reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset |
| Annotation queue items | ax annotation-queues CLI (below) and/or the Arize UI; configs must exist |
Always ensure the relevant annotation config exists in the space before expecting labels to persist.
Basic CRUD: Annotation Configs
List
ax annotation-configs list --space SPACE
ax annotation-configs list --space SPACE -o json
ax annotation-configs list --space SPACE --limit 20
Create — Categorical
Categorical configs present a fixed set of labels for reviewers to choose from.
ax annotation-configs create \
--name "Correctness" \
--space SPACE \
--type categorical \
--value correct \
--value incorrect \
--optimization-direction maximize
Common binary label pairs:
correct/incorrecthelpful/unhelpfulsafe/unsaferelevant/irrelevantpass/fail
Create — Continuous
Continuous configs let reviewers enter a numeric score within a defined range.
ax annotation-configs create \
--name "Quality Score" \
--space SPACE \
--type continuous \
--min-score 0 \
--max-score 10 \
--optimization-direction maximize
Create — Freeform
Freeform configs collect open-ended text feedback. No additional flags needed beyond name, space, and type.
ax annotation-configs create \
--name "Reviewer Notes" \
--space SPACE \
--type freeform
Get
ax annotation-configs get NAME_OR_ID
ax annotation-configs get NAME_OR_ID -o json
ax annotation-configs get NAME_OR_ID --space SPACE # required when using name instead of ID
Delete
ax annotation-configs delete NAME_OR_ID
…
FAQ
What is an annotation config?
It's a set of rules that defines how people should evaluate an item. For example, it could be a list of options (good/bad), a score range (0-10), or a free-text field.
What is an annotation queue?
It's a workflow that organizes who will perform the evaluations and which data they will review. It connects people to the annotation configs.
How are evaluations applied to data?
Evaluations can be applied directly in the Arize interface or through code (Python SDK) that sends the annotations to specific data points.
Details
Original description by the author
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5001
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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