By Arize-ai
Manage the full Arize AX observability lifecycle for LLM applications: auto-instrument code, export traces, create and evaluate datasets, run experiments, optimize prompts, and audit compliance — all through the ax CLI.
Manages Arize users, organizations, spaces, projects, roles, role bindings, resource restrictions, and API keys via the ax CLI. Use for enterprise admin workflows: inviting and offboarding users, onboarding new teams, creating custom roles for SAML/SSO mappings, assigning roles to users, restricting project-level access, and managing service keys for multi-tenant architectures. Covers ax users, ax organizations, ax spaces, ax projects, ax roles, ax role-bindings, and ax api-keys.
Creates, reads, updates, and deletes Arize AI integrations that store LLM provider credentials used by evaluators and other Arize features. Supports any LLM provider (e.g. OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Vertex AI, Gemini, NVIDIA NIM). Use when the user mentions AI integration, LLM provider credentials, create integration, list integrations, update credentials, delete integration, or connecting an LLM provider to Arize.
Creates and manages annotation configs (categorical, continuous, freeform label schemas) and annotation queues (human review workflows) on Arize. Applies human annotations to project spans via the Python SDK. Use when the user mentions annotation config, annotation queue, label schema, human feedback, bulk annotate spans, update_annotations, labeling queue, annotate record, or human review.
INVOKE THIS SKILL when auditing an AI agent or LLM app for regulatory compliance. Covers EU AI Act, GPAI Code of Practice, GDPR, NIST AI RMF, Colorado AI Act, HIPAA, and ISO 42001. Scans the codebase for compliance gaps, cross-references Arize instrumentation for audit trail coverage, and produces an actionable remediation checklist tailored to the selected frameworks.
Creates, manages, and queries Arize datasets and examples. Covers dataset CRUD, appending examples, exporting data, and file-based dataset creation using the ax CLI. Use when the user needs test data, evaluation examples, or mentions create dataset, list datasets, export dataset, append examples, dataset version, golden dataset, or test set.
Own this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimOwn this plugin?
Verify ownership to unlock analytics, metadata editing, and a verified badge. GitHub access is read-only (username + org membership).
Sign in to claimBased on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Skills that guide AI coding agents to help you add observability, run experiments, and optimize prompts for your LLM applications.
These skills encode the workflows we've refined building the Arize platform and helping teams debug LLM apps in production. They handle the ax CLI flags, data shape quirks, and multi-step recipes so you don't have to.
Works with Cursor, Claude Code, Codex, GitHub Copilot, Windsurf, and 40+ other agents.
Adding tracing to your app — give your coding agent this prompt:
Follow the instructions from https://arize.com/docs/PROMPT.md and ask me questions as needed.
This walks through a two-phase flow: analyze your codebase for LLM providers and frameworks, then add Arize AX tracing with the right instrumentors. No skill installation needed.
Already have traces? Give your agent this prompt to install the skills and start debugging:
Install the Arize skills plugin from https://github.com/Arize-ai/arize-skills, then use the arize-trace skill to export and analyze recent traces from my project. Summarize any errors or latency issues you find.
# Interactive — choose skills, agent, and scope
npx skills add Arize-ai/arize-skills
# Non-interactive — install everything with auto-detected defaults
npx skills add Arize-ai/arize-skills --skill "*" --yes
Both options auto-detect your agent (Cursor, Claude Code, Codex, etc.) and symlink skills into place.
macOS / Linux:
git clone https://github.com/Arize-ai/arize-skills.git
cd arize-skills
./install.sh --project ~/my-project
Windows (PowerShell):
git clone https://github.com/Arize-ai/arize-skills.git
cd arize-skills
.\install.ps1 -Project ~\my-project
The installer detects installed agents and optionally installs the ax CLI. Use --global / -Global instead to install to ~/.<agent>/skills/.
If you already have ax installed (v0.9.0+):
ax skills install
Open up a Claude Code session and execute the following:
/plugin marketplace add Arize-ai/arize-skills
/plugin install arize-skills@arize-skills
ax)The skills use the ax CLI to interact with the Arize API. Install it if you don't have it:
# Preferred (isolated environment)
uv tool install arize-ax-cli
# or
pipx install arize-ax-cli
# Fallback
pip install arize-ax-cli
Option A — ax CLI profile (recommended):
Set up your API key once and it persists across all sessions and projects:
# Interactive wizard (creates 'default' profile if no profiles exist)
ax profiles create
# Or pass the key directly (optional profile name as positional arg)
ax profiles create --api-key YOUR_API_KEY
ax profiles create staging --api-key YOUR_STAGING_KEY
# Update an existing profile (patches only what you specify)
ax profiles update --api-key NEW_API_KEY
ax profiles update --region us-east-1b
# Other profile management
ax profiles list
ax profiles show
ax profiles use staging
ax profiles delete staging
You'll also need a space name or ID. Find yours by running ax spaces list -o json (use the name or base64 id), then persist it:
# macOS/Linux — add to ~/.zshrc or ~/.bashrc
export ARIZE_SPACE="my-workspace" # name, or base64 ID like U3BhY2U6...
Option B — Environment variables:
export ARIZE_API_KEY="your-api-key" # from https://app.arize.com/admin > API Keys
export ARIZE_SPACE="my-workspace" # space name or base64 ID from ax spaces list
# export ARIZE_DEFAULT_PROJECT=my-project # optional default project
# export OPENAI_API_KEY="sk-..." # for AI integrations and evaluators
# export ANTHROPIC_API_KEY="sk-ant-..." # for AI integrations and evaluators
ax --version && ax profiles show 2>&1
Point the CLI at your deployment with a single-endpoint profile (hostname and HTTPS port, usually 443). Replace the host with the value your operations team provides:
ax profiles create my-onprem --api-key <key> --single-host arize.yourcompany.com --single-port 443
ax profiles use my-onprem
ax profiles validate
For interactive setup, ax profiles create also offers Advanced → Single endpoint. More options (TOML, Flight/OTLP splits) are documented in the arize-ax-cli README.
Skills for querying and analyzing data from the Arize ML observability platform using GraphQL. Includes trace analysis and general platform analytics.
Automatic tracing of Claude Code sessions to Arize AX or Phoenix with OpenInference spans. Supports 9 hooks for comprehensive observability.
npx claudepluginhub arize-ai/arize-skills --plugin arize-skillsManage resources with the CLI, and debug traces
Skills for querying and analyzing data from the Arize ML observability platform using GraphQL. Includes trace analysis and general platform analytics.
LLM observability tooling for agent development and Claude Code
Claude Code skill pack for Langfuse LLM observability (24 skills)
OpenLit telemetry for Claude Code: sessions, tool calls, edit decisions, and cost rollups.
Skills for tracing, evaluating, and improving AI agents with MLflow. Supports the full agent improvement loop: instrument → trace → evaluate → iterate → validate.