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Phoenix Tracing

Listed
arize-ai·vlatest·devops

Phoenix Tracing identifies and maps out complex, multi-step user journeys within your data, revealing critical drop-off points for optimization.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add arize-ai/phoenix --skill phoenix-tracing -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

arize-ai

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • No undeclared network calls: failed

    Contacts 1 host that is neither well-known infrastructure nor declared in the skill's frontmatter: api.weather.com.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

    Hosts contacted

    api.weather.comundeclared

    What it found

    • .agents/skills/phoenix-tracing/references/instrumentation-manual-python.md:49contacts api.weather.com
      response = requests.get(f"https://api.weather.com/{city}")

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned arize-ai/phoenix at b438233ce4.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
b438233ce4
Manifest
.agents/skills/phoenix-tracing/SKILL.md
Scanned
33 files, 3,413 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to integrate with Arize Phoenix, a platform designed for observability and monitoring of machine learning systems. It allows agents to query performance metrics, analyze model drift, and visualize data pipelines directly within their operational context.

When to use it

  • Investigating sudden drops in model accuracy or prediction confidence scores.
  • Monitoring real-time data quality issues feeding into a training pipeline.
  • Generating visual reports on feature distributions and label consistency over time.
  • Debugging complex ML workflows where traditional logging is insufficient.

Key capabilities

  • Connects AI agents to the Arize Phoenix observability platform.
  • Retrieves detailed metrics regarding model performance and health.
  • Facilitates deep dives into data lineage and pipeline execution history.
  • Supports visualization of model behavior across different environments.

Example prompts

  • "Check the current drift status for our customer churn prediction model in production."
  • "Summarize the last 24 hours of data quality issues detected by Phoenix."
  • "Visualize the feature importance distribution for the latest batch of training data."

Tips & gotchas

Ensure your AI agent has proper authentication credentials to access the Arize Phoenix instance before attempting queries. This skill is specific to the Phoenix ecosystem and will not function with other observability tools like Prometheus or Datadog without additional configuration.