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Arize Annotation

  • 1k installs
  • 37.1k repo stars
  • Updated July 28, 2026
  • github/awesome-copilot

This is a copy of arize-annotation by arize-ai - installs and ranking accrue to the original listing.

arize-annotation is a GitHub awesome-copilot skill that diagnoses and corrects Arize ax profile and authentication problems when an agent cannot connect to the Arize service for developers integrating LLM observability.

About

arize-annotation is a troubleshooting skill from github/awesome-copilot for Arize ax CLI profile setup when authentication fails. It instructs agents to run ax profiles show, interpret missing API keys, absent profiles, 401 Unauthorized responses, and wrong endpoint or region settings, then fix misconfigured profiles reactively—not proactively. Developers reach for arize-annotation when an Arize-connected agent or workflow fails with auth errors, empty profile output, or region mismatches during LLM observability integration. The skill maps common failure signatures to concrete remediation steps so observability pipelines can resume without manual dashboard digging.

  • Runs `ax profiles show` to inspect current API key, region, and profile state
  • Safely updates only broken fields using environment variable references instead of raw keys
  • Creates new profiles when none exist
  • Provides exact remediation steps for 401 errors, missing profiles, and wrong-region issues
  • Never runs checks proactively — triggered only on authentication failure

Arize Annotation by the numbers

  • 1,021 all-time installs (skills.sh)
  • +24 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Security screen: LOW risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
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Listed on Skillselion
Installs1k
repo stars37.1k
Security audit3 / 3 scanners passed
Last updatedJuly 28, 2026
Repositorygithub/awesome-copilot

How do you fix Arize ax profile authentication errors?

Diagnose and correct Arize profile and authentication problems when an agent cannot connect to the Arize service.

Who is it for?

Developers integrating Arize LLM observability who hit 401 errors, missing ax profiles, or wrong region settings during agent or API setup.

Skip if: Developers not using Arize, or teams needing proactive observability dashboard design rather than ax CLI auth repair.

When should I use this skill?

Arize authentication fails with 401, ax profiles show reports no profile or missing API key, or the endpoint/region is misconfigured.

What you get

Corrected ax CLI profile configuration, valid API key, proper region endpoint, and restored Arize service connectivity.

  • configured ax profile
  • valid API key
  • correct region endpoint

Files

SKILL.mdMarkdownGitHub ↗

Arize Annotation Skill

`SPACE` — All --space flags and the ARIZE_SPACE env var accept a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax 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 found or version error → see references/ax-setup.md
  • 401 Unauthorized / missing API key → run ax profiles show to 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 list to pick by name, or ask the user
  • Security: Never read .env files or search the filesystem for credentials. Use ax profiles for Arize credentials and ax ai-integrations for 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.

FieldDescription
NameDescriptive identifier (e.g. Correctness, Helpfulness). Must be unique within the space.
Typecategorical (pick from a list), continuous (numeric range), or freeform (free text).
ValuesFor categorical: array of {"label": str, "score": number} pairs.
Min/Max ScoreFor continuous: numeric bounds.
Optimization DirectionWhether higher scores are better (maximize) or worse (minimize). Used to render trends in the UI.

Where labels get applied (surfaces)

SurfaceTypical path
Project spansPython SDK spans.update_annotations (below) and/or the Arize UI
Dataset examplesArize UI (human labeling flows); configs must exist in the space
Experiment outputsOften reviewed alongside datasets or traces in the UI — see arize-experiment, arize-dataset
Annotation queue itemsax 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 / incorrect
  • helpful / unhelpful
  • safe / unsafe
  • relevant / irrelevant
  • pass / 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:

ColumnRequiredDescription
context.span_idyesThe span to annotate
annotation.<name>.labelone ofCategorical or freeform label
annotation.<name>.scoreone ofNumeric score
annotation.<name>.updated_bynoAnnotator identifier (email or name)
annotation.<name>.updated_atnoTimestamp in milliseconds since epoch
annotation.notesnoFreeform notes on the span

Limitation: Annotations apply only to spans within 31 days prior to submission.

---

Troubleshooting

ProblemSolution
ax: command not foundSee references/ax-setup.md
401 UnauthorizedAPI key may not have access to this space. Verify at https://app.arize.com/admin > API Keys
Annotation config not foundax annotation-configs list --space SPACE (or use ax annotation-configs get NAME_OR_ID --space SPACE)
409 Conflict on createName already exists in the space. Use a different name or get the existing config ID.
Queue not foundax annotation-queues list --space SPACE; verify the queue name or ID
Record not appearing in queueEnsure the annotation config linked to the queue exists; check ax annotation-configs list --space SPACE
Span SDK errors or missing spansConfirm 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.

Related skills

How it compares

Use arize-annotation for reactive ax CLI auth repair; use broader MLOps skills when designing observability pipelines beyond Arize profile errors.

FAQ

When should arize-annotation run?

arize-annotation runs only when Arize authentication fails—401 errors, missing profiles, unset API keys, or wrong regions. The skill explicitly says not to run these checks proactively; invoke it after a connection failure.

What command does arize-annotation start with?

arize-annotation starts with ax profiles show to inspect the current ax CLI state. Agents read whether the API key is unset, no profile exists, a 401 occurs, or the endpoint/region is wrong before applying fixes.

Is Arize Annotation safe to install?

skills.sh reports 3 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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