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Arize AI

AI observability and evaluation

First-party Arize and Phoenix workflows for tracing, evaluations, datasets, experiments, and prompt improvement.

15Skills
37kStars
phoenix-cliBest skill

Featured skills

phoenix-cli

Used to debug LLM applications using the Phoenix CLI (`px`, from @arizeai/phoenix-cli).

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phoenix-evals

Build, run, and validate evaluators for AI/LLM applications with Phoenix, in Python or TypeScript (including Vercel AI...

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phoenix-tracing

OpenInference semantic conventions and instrumentation for Phoenix AI observability.

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arize-admin

Manages Arize users, organizations, spaces, projects, roles, role bindings, resource restrictions, and API keys via the...

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arize-ai-provider-integration

Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and...

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arize-annotation

Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human...

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arize-compliance-audit

INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance.

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arize-dataset

Creates, manages, and queries Arize datasets and examples.

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arize-evaluator

Handles LLM-as-judge and code evaluator workflows on Arize including creating/updating evaluators, running evaluations...

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arize-experiment

Creates, runs, and analyzes Arize experiments for evaluating and comparing model performance.

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arize-instrumentation

Adds Arize AX tracing to an LLM application for the first time.

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arize-link

Generates deep links to the Arize UI for projects, traces, spans, sessions, datasets, experiments, labeling queues,...

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