Agent skills for Arize — datasets, experiments, and traces via the ax CLI
npx skills add https://github.com/arize-ai/arize-skills --skill arize-instrumentationCLI를 사용하여 이 스킬을 설치하고 작업 공간에서 SKILL.md 워크플로 사용을 시작하세요.
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, 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-ai-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 — .env file (project-scoped credentials + provider keys):
Copy the example and fill in your keys:
cp .env.example .env
# Edit .env with your credentials
The .env file supports all credentials used by the skills:
ARIZE_API_KEY=your-api-key # from https://app.arize.com/admin > API Keys
ARIZE_SPACE=my-workspace # space name or base64 ID from ax spaces list
# ARIZE_DEFAULT_PROJECT=my-project # optional default project
# OPENAI_API_KEY=sk-... # for AI integrations and evaluators
# ANTHROPIC_API_KEY=sk-ant-... # for AI integrations and evaluators
Skills automatically load this file during their prerequisite check. The .env file is gitignored — never commit it.
Option C — Environment variables (CI/CD):
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
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.
| Skill | Description |
|---|---|
| arize-trace | Export traces and spans by trace ID, span ID, or session ID. Debug LLM application issues. |
| arize-instrumentation | Add Arize AX tracing to an app. Two-phase flow: analyze codebase, then implement instrumentation (uses Agent-Assisted Tracing). |
| arize-dataset | Create, manage, and download datasets and examples. |
| arize-experiment | Run and analyze experiments against datasets. |
| arize-evaluator | Create LLM-as-judge evaluators, run evaluation tasks, and set up continuous monitoring. |
| arize-ai-provider-integration | Create and manage LLM provider credentials (OpenAI, Anthropic, Azure, Bedrock, Vertex, and more). |
| arize-annotation | Create and manage annotation configs (categorical, continuous, freeform); bulk-annotate project spans via the Python SDK. |
| arize-prompt-optimization | Optimize prompts using trace data, experiments, and meta-prompting. |
| arize-link | Generate deep links to traces, spans, and sessions in the Arize UI. |
Bash (install.sh):
| Flag | Description |
|---|---|
--project <dir> |
Required. Target project directory for skill symlinks |
--global |
Install to ~/.<agent>/skills/ instead (alternative to --project) |
--copy |
Copy files instead of symlinking |
--force |
Overwrite existing skills |
--skip-cli |
Don't install ax CLI even if missing |
--agent <name> |
Manually specify agent (cursor, claude, codex) — repeatable |
--skill <name> |
Only install/uninstall specific skills — repeatable (e.g. --skill arize-trace --skill arize-dataset) |
--yes |
Skip confirmation prompts |
--list |
List all available skills and exit |
--uninstall |
Remove previously installed skill symlinks |
PowerShell (install.ps1):
| Flag | Description |
|---|---|
-Project <dir> |
Required. Target project directory for skill symlinks |
-Global |
Install to ~/.<agent>/skills/ instead (alternative to -Project) |
-Copy |
Copy files instead of symlinking |
-Force |
Overwrite existing skills |
-SkipCli |
Don't install ax CLI even if missing |
-Agent <name> |
Manually specify agent (cursor, claude, codex) — repeatable |
-Skill <name> |
Only install/uninstall specific skills — repeatable |
-Yes |
Skip confirmation prompts |
-Uninstall |
Remove previously installed skill symlinks |
-List |
List all available skills and exit |
npx skills updatecd arize-skills && git pull (symlinks update automatically)tests/run_skill.py is an interactive test harness that runs a skill end-to-end using the Claude Agent SDK. It creates a temporary workspace, passes in your Arize credentials, and streams the agent's output.
python tests/run_skill.py --skill arize-trace --prompt "Export trace abc123"
[!WARNING]
Configure.claude/settings.jsonbefore running the test harnessThe test harness uses Claude Code's
bypassPermissionsmode, which skips all interactive
approval prompts. This is safe because the agent runs in a sandboxed temporary workspace —
but only if yoursettings.jsonhas a denylist blocking dangerous shell commands.Without this,
bypassPermissionsgives the agent unrestricted shell access.Add the following to
.claude/settings.jsonin this repo (create it if it doesn't exist):{ "permissions": { "deny": [ "Bash(rm -rf*)", "Bash(curl*)", "Bash(wget*)", "Bash(ssh*)", "Bash(scp*)", "Bash(git push*)", "Bash(sudo*)", "Bash(chmod*)", "Bash(chown*)" ] } }
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