arize-skills

agent
Security Audit
Fail
Health Warn
  • License — License: MIT
  • Description — Repository has a description
  • Active repo — Last push 0 days ago
  • Low visibility — Only 6 GitHub stars
Code Fail
  • rm -rf — Recursive force deletion command in install.sh
  • rm -rf — Recursive force deletion command in tests/run_skill.py
Permissions Pass
  • Permissions — No dangerous permissions requested
Purpose
This project provides agent skills that guide AI coding assistants to add observability, run experiments, and optimize prompts for LLM applications using the Arize platform. It handles the underlying CLI configurations and workflows so developers do not have to manage them manually.

Security Assessment
The tool does not request explicitly dangerous permissions or contain hardcoded secrets. However, it does require API keys for authentication to make external network requests to the Arize API. The most significant security concern is the presence of recursive force deletion commands (`rm -rf`) detected in both the `install.sh` script and `tests/run_skill.py` file. If these scripts are executed with elevated privileges or unintended paths, they could accidentally delete critical system files. Overall risk is rated as Medium.

Quality Assessment
The repository is actively maintained, having received a push very recently, and is protected by a standard MIT license. On the downside, the project suffers from extremely low community visibility, evidenced by having only 6 GitHub stars. Consequently, the codebase has not been broadly reviewed by the open-source community, making its long-term reliability harder to guarantee.

Verdict
Use with caution — the core utility is functional and actively updated, but unverified `rm -rf` operations in the installation scripts warrant a thorough manual review of those files before execution.
SUMMARY

Agent skills for Arize — datasets, experiments, and traces via the ax CLI

README.md

Arize Skills

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.

New to Arize? Start Here

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.

Installation

Option 1: npx (recommended)

# 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.

Option 2: git clone

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/.

Option 3: Claude Code plugin

/plugin add https://github.com/Arize-ai/arize-skills

Prerequisites

Arize CLI (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

Authentication

Option A — ax CLI profile (recommended):

Set up your API key once and it persists across all sessions and projects:

# Interactive wizard
ax profiles create

# Or pass the key directly
ax profiles create --api-key YOUR_API_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

You'll also need a space ID. Find yours in the Arize URL (/spaces/{SPACE_ID}/...) or run ax spaces list -o json, then persist it:

# macOS/Linux — add to ~/.zshrc or ~/.bashrc
export ARIZE_SPACE_ID="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_ID=U3BhY2U6...              # base64 space ID from your Arize URL
# 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_ID="U3BhY2U6..."       # base64 space ID from your Arize URL

Verify

ax --version && ax profiles show 2>&1

Available Skills

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.

Installer Flags

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

Updating

  • npx path: npx skills update
  • git clone path: cd arize-skills && git pull (symlinks update automatically)

Testing Skills

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.json before running the test harness

The test harness uses Claude Code's bypassPermissions mode, which skips all interactive
approval prompts
. This is safe because the agent runs in a sandboxed temporary workspace —
but only if your settings.json has a denylist blocking dangerous shell commands.

Without this, bypassPermissions gives the agent unrestricted shell access.

Add the following to .claude/settings.json in 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*)"
    ]
  }
}

Links

License

Apache 2.0

Reviews (0)

No results found