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Phoenix Tracing

OrganizationPopular
Arize-ai
phoenix-tracing

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Overview

PublisherArize-ai
Repositoryphoenix
Skill namephoenix-tracing
Stars
11.8K
Forks
1.2K
Bundled files
32
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 32 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by Arize-ai on GitHub. Read the source before you install it.

Installation

Install the Phoenix Tracing AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/phoenix-tracing
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/Arize-ai/phoenix.git /tmp/phoenix
mkdir -p .claude/skills
cp -r /tmp/phoenix/.agents/skills/phoenix-tracing .claude/skills/phoenix-tracing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Phoenix Tracing in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Phoenix Tracing on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Phoenix Tracing is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Phoenix Tracing

Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment.

When to Apply

Reference these guidelines when:

  • Setting up Phoenix tracing (Python or TypeScript)
  • Creating custom spans for LLM operations
  • Adding attributes following OpenInference conventions
  • Deploying tracing to production
  • Querying and analyzing trace data

Reference Categories

PriorityCategoryDescriptionPrefix
1SetupInstallation and configurationsetup-*
2InstrumentationAuto and manual tracinginstrumentation-*
3Span Types9 span kinds with attributesspan-*
4OrganizationProjects and sessionsprojects-*, sessions-*
5EnrichmentCustom metadatametadata-*
6ProductionBatch processing, maskingproduction-*
7FeedbackAnnotations and evaluationannotations-*

Quick Reference

1. Setup (START HERE)

2. Instrumentation

3. Span Types (with full attribute schemas)

4. Organization

5. Enrichment

6. Production (CRITICAL)

7. Feedback

Reference Files

Common Workflows

  • Quick Start: setup-{lang} → instrumentation-auto-{lang} → Check Phoenix
  • Custom Spans: setup-{lang} → instrumentation-manual-{lang} → span-{type}
  • Session Tracking: sessions-{lang} for conversation grouping patterns
  • Production: production-{lang} for batching, masking, and deployment

How to Use This Skill

Navigation Patterns:

bash
# By category prefix
references/setup-*              # Installation and configuration
references/instrumentation-*    # Auto and manual tracing
references/span-*               # Span type specifications
references/sessions-*           # Session tracking
references/production-*         # Production deployment
references/fundamentals-*       # Core concepts

# By language
references/*-python.md          # Python implementations
references/*-typescript.md      # TypeScript implementations

Reading Order:

  1. Start with setup-{lang} for your language
  2. Choose instrumentation-auto-{lang} OR instrumentation-manual-{lang}
  3. Reference span-{type} files as needed for specific operations
  4. See fundamentals-* files for attribute specifications

References

Phoenix Documentation:

Python API Documentation:

TypeScript API Documentation:

  • TypeScript Packages - @arizeai/phoenix-otel, @arizeai/phoenix-client, and other TypeScript packages

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Phoenix Tracing AI skill do?

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Why use Phoenix Tracing on TypingMind?

Because you install it once and use it with any model. Phoenix Tracing is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Phoenix Tracing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Arize-ai/phoenix/tree/main/.agents/skills/phoenix-tracing. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Phoenix Tracing?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Phoenix Tracing?

As many as you like. As long as a model supports skills, you can use Phoenix Tracing with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Phoenix Tracing AI skill free?

Yes. It is published on GitHub by Arize-ai under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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