Cria e gerencia configurações de anotação e filas de revisão humana para aplicar rótulos a spans de projeto e conjuntos de dados.
- Estrelas no GitHub
- 41.038
- Arquivos
- 3
- Último commit
- 11 de nov. de 2026
- Licença
- MIT
- Ferramentas permitidas
- —
O que a skill arize-annotation faz?
arize-annotation é uma Agent Skill. Cria e gerencia configurações de anotação e filas de revisão humana para aplicar rótulos a spans de projeto e conjuntos de dados.
Resumo gerado a partir do SKILL.md da skill.
Sobre a skill arize-annotation
Gerenciar loops de feedback humano para modelos de machine learning é o que esta SKILL permite para desenvolvedores que utilizam o Arize. Ela fornece as ferramentas para definir esquemas de rótulos por meio de configurações de anotação e organizar fluxos de trabalho de revisão humana via filas de anotação. Os usuários podem criar esquemas categóricos, contínuos ou de formato livre para capturar tipos específicos de feedback, como correção ou pontuações de qualidade.
Um agente pode usar esta SKILL quando um usuário precisar configurar um processo de rotulagem ou gerenciar filas de revisão existentes. Ela depende da ax CLI para gerenciar configurações e filas, e utiliza o Arize Python SDK para atualizações em massa de spans de projeto com rótulos pré-existentes. Isso permite a aplicação programática de anotações a spans, exemplos de datasets e registros de experimentos. Note que as atualizações em massa de spans são limitadas a spans dos últimos 31 dias.
O que um agente pode fazer com ela
- 1Configurar um fluxo de trabalho de revisão humanaEstabelecer uma fila de anotação que encaminha registros específicos para um revisor via e-mail.
- 2Atualizar spans de modelo em massaAplicar um grande conjunto de rótulos fornecidos por humanos aos spans do projeto usando um DataFrame do Python.
Escrito a partir do SKILL.md da skill. Cada caso de uso é verificado com uma citação dele.
Como instalar a skill arize-annotation
Cada comando copia a pasta da skill para o diretório de skills do cliente. Reinicie o cliente ou inicie uma nova sessão para carregá-la.Instalar a skill arize-annotation no Claude Code
~/.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotationInstalar a skill arize-annotation no Claude Code (project)
.claude/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation .claude/skills/arize-annotationInstalar a skill arize-annotation no Codex
~/.agents/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.agents/skills/arize-annotationInstalar a skill arize-annotation no Gemini CLI
~/.gemini/skills/arize-annotation/SKILL.md
$ npx degit github/awesome-copilot/skills/arize-annotation ~/.gemini/skills/arize-annotationInstalar a skill arize-annotation no 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
Verificações automatizadas
Executado em 15 de nov. de 2026Relatório completo →Passou nas nossas verificações automáticas. Não é uma auditoria de segurança: leia os scripts antes de deixar um agente executá-los. Como as verificações funcionam
- Repositório de origem vinculado e acessívelgithub/awesome-copilot/skills/arize-annotation is public and reachablegithub.com/github/awesome-copilot/tree/HEAD/skills/arize-ann ↗Passou
- Licença de código aberto (OSI)MITspdx.org/licenses/MIT.html ↗Passou
- Commit ou release no último anoLast commit 2026-11-11github.com/github/awesome-copilot/commits ↗Passou
- Sem baixar e executar nem roubo de credenciais nos scriptsNo scripts in the skill folderPassou
- Sem instruções ocultas no SKILL.mdNo override phrases, safeguard switches, credential requests or hidden charactersgithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗Passou
- Ferramentas pré-aprovadas são restritasNo pre-approved tools; the agent asks before using onegithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗Passou
- O SKILL.md segue o formato Agent SkillsValid name and description; says when to use it; 301 linesgithub.com/github/awesome-copilot/blob/HEAD/skills/arize-ann ↗Passou
SKILL.md
Ver no 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.
Arquivos desta skill
3Perguntas sobre a skill arize-annotation
Como instalo a skill arize-annotation no Claude Code?
Execute npx degit github/awesome-copilot/skills/arize-annotation ~/.claude/skills/arize-annotation. O Claude Code carrega skills de ~/.claude/skills (todos os projetos) ou .claude/skills (um projeto) e usa uma skill quando a descrição dela corresponde à tarefa.
Quais clientes podem usar a skill arize-annotation?
Qualquer cliente que leia o formato Agent Skills: Claude Code, Codex, Gemini CLI e claude.ai (envie como ZIP). Scripts da skill são executados apenas onde o cliente permite execução de código.
Como posso aplicar anotações em massa aos spans do projeto?
Você pode usar o método client.spans.update_annotations do Arize Python SDK com um DataFrame do pandas contendo os IDs dos spans e os valores das anotações.
O que acontece se eu excluir uma configuração de anotação?
A exclusão é irreversível, e quaisquer associações de filas de anotação a essa configuração também serão removidas no produto.