Skill arize-annotation
✓ VerificadoCrea y gestiona configuraciones de anotación y colas de revisión humana para aplicar etiquetas a spans y datasets de proyectos.
- Estrellas en GitHub
- 41.038
- Archivos
- 3
- Último commit
- 11 nov 2026
- Licencia
- MIT
- Herramientas permitidas
- —
¿Qué hace la skill arize-annotation?
arize-annotation es una agent skill. Crea y gestiona configuraciones de anotación y colas de revisión humana para aplicar etiquetas a spans y datasets de proyectos.
Resumen generado a partir del SKILL.md de la skill.
Sobre la skill arize-annotation
Esta SKILL permite a los desarrolladores que utilizan Arize gestionar bucles de retroalimentación humana para modelos de machine learning. Proporciona las herramientas necesarias para definir esquemas de etiquetas mediante configuraciones de anotación y organizar flujos de trabajo de revisión humana a través de colas de anotación. Los usuarios pueden crear esquemas categóricos, continuos o de texto libre para capturar tipos específicos de feedback, como la corrección o puntuaciones de calidad.
Un agente puede utilizar esta SKILL cuando un usuario necesite configurar un proceso de etiquetado o gestionar colas de revisión existentes. Se apoya en la CLI de ax para gestionar configuraciones y colas, y utiliza el Arize Python SDK para actualizar masivamente los spans del proyecto con etiquetas preexistentes. Esto permite la aplicación programática de anotaciones a spans, ejemplos de datasets y registros de experimentos. Ten en cuenta que las actualizaciones masivas de spans están limitadas a los spans de los últimos 31 días.
Lo que un agente puede hacer con ella
- 1Configurar un flujo de trabajo de revisión humanaEstablecer una cola de anotación que envíe registros específicos a un revisor por correo electrónico.
- 2Actualizar masivamente los spans del modeloAplicar un gran conjunto de etiquetas proporcionadas por humanos a los spans del proyecto utilizando un DataFrame de Python.
Escrito a partir del SKILL.md de la skill. Cada caso de uso se verifica con una cita del mismo.
Cómo instalar la skill arize-annotation
Cada comando copia la carpeta de la skill al directorio de skills del cliente. Reinicia el cliente o inicia una nueva sesión para cargarla.Instalar la skill arize-annotation en Claude Code
~/.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotationInstalar la skill arize-annotation en Claude Code (project)
.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation .claude/skills/arize-annotationInstalar la skill arize-annotation en Codex
~/.agents/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.agents/skills/arize-annotationInstalar la skill arize-annotation en Gemini CLI
~/.gemini/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.gemini/skills/arize-annotationInstalar la skill arize-annotation en Claude.ai
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
Verificaciones automáticas
Ejecutado 13 nov 2026Informe completo →Superó nuestras comprobaciones automáticas. No es una auditoría de seguridad: lee los scripts antes de dejar que un agente los ejecute. Cómo funcionan las verificaciones
- Repositorio de origen vinculado y accesiblegithub/awesome-copilot/skills/arize-annotation is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/arize-ann ↗Aprobado
- Licencia de código abierto (OSI)MITspdx.org/licenses/MIT.html ↗Aprobado
- Commit o lanzamiento en el último añoLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Aprobado
- Sin descargar y ejecutar ni robo de credenciales en los scriptsNo scripts in the skill folderAprobado
- Sin instrucciones ocultas en SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗Aprobado
- Las herramientas preaprobadas están acotadasNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗Aprobado
SKILL.md
Ver en GitHubArize 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.
Archivos de esta skill
3Preguntas sobre la skill arize-annotation
¿Cómo instalo la skill arize-annotation en Claude Code?
Ejecuta npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotation. Claude Code carga las skills desde ~/.claude/skills (todos los proyectos) o .claude/skills (un proyecto) y usa una skill cuando su descripción coincide con la tarea.
¿Qué clientes pueden usar la skill arize-annotation?
Cualquier cliente que lea el formato Agent Skills: Claude Code, Codex, Gemini CLI y claude.ai (subir como ZIP). Los scripts de la skill se ejecutan solo donde el cliente permite la ejecución de código.
¿Cómo puedo aplicar anotaciones de forma masiva a los spans del proyecto?
Puedes usar el método client.spans.update_annotations del Arize Python SDK con un DataFrame de pandas que contenga los IDs de los spans y los valores de las anotaciones.
¿Qué sucede si elimino una configuración de anotación?
La eliminación es irreversible, y cualquier asociación de colas de anotación con esa configuración también se eliminará en el producto.