All skills
github avatar

/arize-evaluator

@4e136f3 official
by githubgithub/awesome-copilot40k stars
5,040

Handles LLM-as-judge evaluation workflows on Arize including creating/updating evaluators, running evaluations on spans or experiments, managing tasks, trigger-run operations, column mapping, and continuous monitoring. Use when the user mentions create evaluator, LLM judge, hallucination, faithfulness, correctness, relevance, run eval, score spans, score experiment, trigger-run, column mapping, continuous monitoring, or improve evaluator prompt.

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/arize-evaluator

This session only. Nothing lands on disk.

referencesax-profiles.md

≈1.2k tokens on demand. Your agent reads this file only when SKILL.md points to it.

ax Profile Setup

Consult this when authentication fails (401, missing profile, missing API key). Do NOT run these checks proactively.

Use this when there is no profile, or a profile has incorrect settings (wrong API key, wrong region, etc.).

1. Inspect the current state

ax profiles show

Look at the output to understand what's configured:

  • API Key: (not set) or missing → key needs to be created/updated
  • No profile output or "No profiles found" → no profile exists yet
  • Connected but getting 401 Unauthorized → key is wrong or expired
  • Connected but wrong endpoint/region → region needs to be updated

2. Fix a misconfigured profile

If a profile exists but one or more settings are wrong, patch only what's broken.

Never pass a raw API key value as a flag. Always reference it via the ARIZE_API_KEY environment variable. If the variable is not already set in the shell, instruct the user to set it first, then run the command:

# If ARIZE_API_KEY is already exported in the shell:
ax profiles update --api-key $ARIZE_API_KEY

# Fix the region (no secret involved — safe to run directly)
ax profiles update --region us-east-1b

# Fix both at once
ax profiles update --api-key $ARIZE_API_KEY --region us-east-1b

update only changes the fields you specify — all other settings are preserved. If no profile name is given, the active profile is updated.

3. Create a new profile

If no profile exists, or if the existing profile needs to point to a completely different setup (different org, different region):

Always reference the key via $ARIZE_API_KEY, never inline a raw value.

# Requires ARIZE_API_KEY to be exported in the shell first
ax profiles create --api-key $ARIZE_API_KEY

# Create with a region
ax profiles create --api-key $ARIZE_API_KEY --region us-east-1b

# Create a named profile
ax profiles create work --api-key $ARIZE_API_KEY --region us-east-1b

To use a named profile with any ax command, add -p NAME:

ax spans export PROJECT -p work

4. Getting the API key

Never ask the user to paste their API key into the chat. Never log, echo, or display an API key value.

If ARIZE_API_KEY is not already set, instruct the user to export it in their shell:

export ARIZE_API_KEY="..."   # user pastes their key here in their own terminal

They can find their key at https://app.arize.com/admin > API Keys. Recommend they create a scoped service key (not a personal user key) — service keys are not tied to an individual account and are safer for programmatic use. Keys are space-scoped — make sure they copy the key for the correct space.

Once the user confirms the variable is set, proceed with ax profiles create --api-key $ARIZE_API_KEY or ax profiles update --api-key $ARIZE_API_KEY as described above.

5. Verify

After any create or update:

ax profiles show

Confirm the API key and region are correct, then retry the original command.

Space

There is no profile flag for space. Save it as an environment variable — accepts a space name (e.g., my-workspace) or a base64 space ID (e.g., U3BhY2U6...). Find yours with ax spaces list -o json.

macOS/Linux — add to ~/.zshrc or ~/.bashrc:

export ARIZE_SPACE="my-workspace"    # name or base64 ID

Then source ~/.zshrc (or restart terminal).

Windows (PowerShell):

[System.Environment]::SetEnvironmentVariable('ARIZE_SPACE', 'my-workspace', 'User')

Restart terminal for it to take effect.

Save Credentials for Future Use

At the end of the session, if the user manually provided any credentials during this conversation and those values were NOT already loaded from a saved profile or environment variable, offer to save them.

Skip this entirely if:

  • The API key was already loaded from an existing profile or ARIZE_API_KEY env var
  • The space was already set via ARIZE_SPACE env var
  • The user only used base64 project IDs (no space was needed)

How to offer: Use AskQuestion: "Would you like to save your Arize credentials so you don't have to enter them next time?" with options "Yes, save them" / "No thanks".

If the user says yes:

  1. API key — Run ax profiles show to check the current state. Then run ax profiles create --api-key $ARIZE_API_KEY or ax profiles update --api-key $ARIZE_API_KEY (the key must already be exported as an env var — never pass a raw key value).

  2. Space — See the Space section above to persist it as an environment variable.

Source: SKILL.md on GitHub

1 warning16d4 checks · Risk MEDIUM
  • Gen Agent Trust Hub16d

    This skill interacts with the Arize 'ax' CLI to manage LLM evaluation workflows. It is generally safe but includes persistence mechanisms to save configuration to shell profiles and uses runtime Python execution for data formatting. It also creates a potential surface for indirect prompt injection as it processes external trace and experiment data using LLM-as-judge templates.

  • Socket16d

    No alerts

  • Snyk16d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    1 finding · Score: 86/100

Signed by skilld at 4e136f3. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub 20 hours ago.

Activeupdated 5 months ago
metadata
{
  "author": "arize",
  "version": "1.0"
}
compatibility
Requires the ax CLI and a configured Arize profile with an AI integration.
  • arize
  • llm-as-judge
  • evaluation
  • monitoring
  • span-scoring
  • prompt-engineering
  • ai-integration
  • experiment-analysis

README badge

README badge for github/awesome-copilot/arize-evaluator

Manages LLM-as-judge evaluators on Arize, including creating evaluators with custom prompts, running evaluations against spans or experiments, column mapping, and continuous monitoring of new traces. Requires the ax CLI and an Arize profile with LLM provider credentials configured via AI integrations.

Generated from the current SKILL.md.

What LLM providers does this skill support?
OpenAI, Anthropic, Azure, Bedrock, Vertex, Gemini, NVIDIA NIM, and custom providers. Credentials are managed via the arize-ai-provider-integration skill or the ax ai-integrations command.
Can I run evaluators on experiment data or only live project traces?
Both. Tasks can attach evaluators to a project for continuous or one-time scoring of live spans, or to a dataset/experiment for backfill evaluation of experiment runs.
What happens if an evaluation fails or is cancelled?
The skill reports the failure and explains what went wrong — it never fabricates or estimates evaluation scores. You should fix the identified issue and retry, or verify integration credentials with ax ai-integrations list.
Does this skill handle evaluator versioning?
Yes. Creating a new version with ax evaluators create-version preserves the old version as immutable while the new version becomes active. This lets you update the prompt or model without affecting past evaluation runs.
What is the difference between span, trace, and session granularity?
Span evaluates individual LLM calls; trace evaluates all spans in a call chain grouped by trace_id; session evaluates all traces in a conversation grouped by session.id. The {conversation} template variable is only available at session granularity.

Generated from the current SKILL.md. These answers refresh after source changes.