arize-annotation skill
✓ 인증됨프로젝트 span 및 데이터셋에 레이블을 적용하기 위한 어노테이션 설정과 휴먼 리뷰 큐를 생성하고 관리합니다.
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- 마지막 커밋
- 2026년 11월 11일
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- MIT
- 허용된 도구
- —
arize-annotation skill은 무엇을 하나요?
arize-annotation은(는) agent skill입니다. 프로젝트 span 및 데이터셋에 레이블을 적용하기 위한 어노테이션 설정과 휴먼 리뷰 큐를 생성하고 관리합니다.
skill의 SKILL.md에서 생성된 요약입니다.
arize-annotation skill 정보
Arize를 사용하는 개발자가 머신러닝 모델을 위한 휴먼 피드백 루프를 관리할 수 있도록 지원하는 SKILL입니다. 어노테이션 설정을 통해 레이블 스키마를 정의하고, 어노테이션 큐를 통해 휴먼 리뷰 워크플로우를 구성할 수 있는 도구를 제공합니다. 사용자는 범주형, 연속형 또는 자유 형식의 스키마를 생성하여 정확성이나 품질 점수와 같은 특정 유형의 피드백을 캡처할 수 있습니다.
사용자가 레이블링 프로세스를 설정하거나 기존 리뷰 큐를 관리해야 할 때 에이전트가 이 SKILL을 사용할 수 있습니다. 설정 및 큐 관리를 위해 ax CLI를 사용하며, 기존 레이블로 프로젝트 span을 일괄 업데이트할 때는 Arize Python SDK를 활용합니다. 이를 통해 span, 데이터셋 예시 및 실험 레코드에 어노테이션을 프로그래밍 방식으로 적용할 수 있습니다. 단, span 일괄 업데이트는 최근 31일 이내의 span으로 제한됩니다.
agent가 수행할 수 있는 작업
- 1휴먼 리뷰 워크플로우 설정특정 레코드를 이메일을 통해 리뷰어에게 전달하는 어노테이션 큐를 구축합니다.
- 2모델 span 일괄 업데이트Python DataFrame을 사용하여 대량의 휴먼 레이블을 프로젝트 span에 적용합니다.
skill의 SKILL.md를 기반으로 작성되었습니다. 모든 사용 사례는 해당 문서의 인용구와 대조 확인되었습니다.
arize-annotation skill 설치 방법
각 명령은 skill 폴더를 클라이언트의 skills 디렉터리에 복사합니다. 클라이언트를 재시작하거나 새 세션을 시작하여 로드하세요.Claude Code에 arize-annotation skill 설치
~/.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotationClaude Code (project)에 arize-annotation skill 설치
.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation .claude/skills/arize-annotationCodex에 arize-annotation skill 설치
~/.agents/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.agents/skills/arize-annotationGemini CLI에 arize-annotation skill 설치
~/.gemini/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.gemini/skills/arize-annotationClaude.ai에 arize-annotation skill 설치
npx degit github/awesome-copilot/skills/arize-annotation arize-annotation && zip -r arize-annotation.zip arize-annotation # claude.ai → Customize → Skills → + → Create skill → Upload a skill → arize-annotation.zip
자동 검사
2026년 11월 13일에 실행됨전체 보고서 →자동 검사를 통과했습니다. 보안 감사는 아닙니다. 에이전트가 실행하기 전에 스크립트를 읽어 보세요. 검사 방식
- 소스 저장소 연결 및 접근 가능github/awesome-copilot/skills/arize-annotation is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/arize-ann ↗통과
- 오픈 소스 라이선스 (OSI)MITspdx.org/licenses/MIT.html ↗통과
- 최근 1년 내 커밋 또는 릴리스Last commit 2026-11-11github.com/github/awesome-copilot/commits ↗통과
- 스크립트에 다운로드 후 실행이나 자격 증명 탈취가 없음No scripts in the skill folder통과
- SKILL.md에 숨겨진 지침 없음No override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗통과
- 사전 허용 도구가 제한됨No pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗통과
SKILL.md
GitHub에서 보기Arize 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
ax annotation-configs delete NAME_OR_ID --space SPACE # required when using name instead of ID
ax annotation-configs delete NAME_OR_ID --force # skip confirmation
Note: Deletion is irreversible. Any annotation queue associations to this config are also removed in the product (queues may remain; fix associations in the Arize UI if needed).
Annotation Queues: ax annotation-queues
Annotation queues route records (spans, dataset examples, experiment runs) to human reviewers. Each queue is linked to one or more annotation configs that define what labels reviewers can apply.
List / Get
ax annotation-queues list --space SPACE
ax annotation-queues list --space SPACE -o json
ax annotation-queues get NAME_OR_ID --space SPACE
ax annotation-queues get NAME_OR_ID --space SPACE -o json
Create
At least one --annotation-config-id is required.
ax annotation-queues create \
--name "Correctness Review" \
--space SPACE \
--annotation-config-id CONFIG_ID \
--annotator-email reviewer@example.com \
--instructions "Label each response as correct or incorrect." \
--assignment-method all # or: random
Repeat --annotation-config-id and --annotator-email to attach multiple configs or reviewers.
Update
List flags (--annotation-config-id, --annotator-email) fully replace existing values when provided — pass all desired values, not just the new ones.
ax annotation-queues update NAME_OR_ID --space SPACE --name "New Name"
ax annotation-queues update NAME_OR_ID --space SPACE --instructions "Updated instructions"
ax annotation-queues update NAME_OR_ID --space SPACE \
--annotation-config-id CONFIG_ID_A \
--annotation-config-id CONFIG_ID_B
Delete
ax annotation-queues delete NAME_OR_ID --space SPACE
ax annotation-queues delete NAME_OR_ID --space SPACE --force # skip confirmation
List Records
ax annotation-queues list-records NAME_OR_ID --space SPACE
ax annotation-queues list-records NAME_OR_ID --space SPACE --limit 50 -o json
Submit an Annotation for a Record
Annotations are upserted by config name — call once per annotation config. Supply at least one of --score, --label, or --text.
ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
--annotation-name "Correctness" \
--label "correct" \
--space SPACE
ax annotation-queues annotate-record NAME_OR_ID RECORD_ID \
--annotation-name "Quality Score" \
--score 8.5 \
--text "Response was accurate but slightly verbose." \
--space SPACE
Assign a Record
Assign users to review a specific record:
ax annotation-queues assign-record NAME_OR_ID RECORD_ID --space SPACE
Delete Records
ax annotation-queues delete-records NAME_OR_ID --space SPACE
Applying Annotations to Spans (Python SDK)
Use the Python SDK to bulk-apply annotations to project spans when you already have labels (e.g., from a review export or an external labeling tool).
import pandas as pd
from arize import ArizeClient
import os
client = ArizeClient(api_key=os.environ["ARIZE_API_KEY"])
# Build a DataFrame with annotation columns
# Required: context.span_id + at least one annotation.<name>.label or annotation.<name>.score
annotations_df = pd.DataFrame([
{
"context.span_id": "span_001",
"annotation.Correctness.label": "correct",
"annotation.Correctness.updated_by": "reviewer@example.com",
},
{
"context.span_id": "span_002",
"annotation.Correctness.label": "incorrect",
"annotation.Correctness.updated_by": "reviewer@example.com",
},
])
response = client.spans.update_annotations(
space_id=os.environ["ARIZE_SPACE"],
project_name="your-project",
dataframe=annotations_df,
validate=True,
)
DataFrame column schema:
| Column | Required | Description |
|---|---|---|
context.span_id |
yes | The span to annotate |
annotation.<name>.label |
one of | Categorical or freeform label |
annotation.<name>.score |
one of | Numeric score |
annotation.<name>.updated_by |
no | Annotator identifier (email or name) |
annotation.<name>.updated_at |
no | Timestamp in milliseconds since epoch |
annotation.notes |
no | Freeform notes on the span |
Limitation: Annotations apply only to spans within 31 days prior to submission.
Troubleshooting
| Problem | Solution |
|---|---|
ax: command not found |
See references/ax-setup.md |
401 Unauthorized |
API key may not have access to this space. Verify at https://app.arize.com/admin > API Keys |
Annotation config not found |
ax annotation-configs list --space SPACE (or use ax annotation-configs get NAME_OR_ID --space SPACE) |
409 Conflict on create |
Name already exists in the space. Use a different name or get the existing config ID. |
| Queue not found | ax annotation-queues list --space SPACE; verify the queue name or ID |
| Record not appearing in queue | Ensure the annotation config linked to the queue exists; check ax annotation-configs list --space SPACE |
| Span SDK errors or missing spans | Confirm project_name, space_id, and span IDs; use arize-trace to export spans |
Related Skills
- arize-trace: Export spans to find span IDs and time ranges
- arize-dataset: Find dataset IDs and example IDs
- arize-evaluator: Automated LLM-as-judge alongside human annotation
- arize-experiment: Experiments tied to datasets and evaluation workflows
- arize-link: Deep links to annotation configs and queues in the Arize UI
Save Credentials for Future Use
See references/ax-profiles.md § Save Credentials for Future Use.
이 스킬의 파일
3arize-annotation skill 질문
Claude Code에 arize-annotation skill을 어떻게 설치하나요?
npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotation를 실행합니다. Claude Code는 ~/.claude/skills(모든 프로젝트) 또는 .claude/skills(한 프로젝트)에서 skills를 로드하며, 설명이 작업과 일치할 때 skill을 사용합니다.
arize-annotation skill을 사용할 수 있는 클라이언트는 무엇인가요?
Agent Skills 형식을 읽는 모든 클라이언트(Claude Code, Codex, Gemini CLI, claude.ai(ZIP으로 업로드))가 사용할 수 있습니다. skill 내 스크립트는 클라이언트가 코드 실행을 허용하는 경우에만 실행됩니다.
프로젝트 span에 어노테이션을 일괄 적용하려면 어떻게 하나요?
Arize Python SDK의 client.spans.update_annotations 메서드를 사용하며, span ID와 어노테이션 값이 포함된 pandas DataFrame을 전달하면 됩니다.
어노테이션 설정을 삭제하면 어떻게 되나요?
삭제는 되돌릴 수 없으며, 해당 설정과 연결된 모든 어노테이션 큐도 제품 내에서 함께 삭제됩니다.