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Skills/arize-ai/phoenix/phoenix-tracing
phoenix-tracing logo

phoenix-tracing

arize-ai/phoenix
712 installs10K stars
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Installation

npx skills add https://github.com/arize-ai/phoenix --skill phoenix-tracing

Summary

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

SKILL.md

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)

  • setup-python - Install arize-phoenix-otel, configure endpoint
  • setup-typescript - Install @arizeai/phoenix-otel, configure endpoint

2. Instrumentation

  • instrumentation-auto-python - Auto-instrument OpenAI, LangChain, etc. (also covers OTel GenAI native instrumentation)
  • instrumentation-auto-typescript - Auto-instrument supported frameworks
  • instrumentation-manual-python - Custom spans with decorators
  • instrumentation-manual-typescript - Custom spans with wrappers
  • instrumentation-atif-python - Import ATIF agent trajectories (Claude Code, OpenHands, Codex, etc.)

3. Span Types (with full attribute schemas)

  • span-llm - LLM API calls (model, tokens, messages, cost)
  • span-chain - Multi-step workflows and pipelines
  • span-retriever - Document retrieval (documents, scores)
  • span-tool - Function/API calls (name, parameters)
  • span-agent - Multi-step reasoning agents
  • span-embedding - Vector generation
  • span-reranker - Document re-ranking
  • span-guardrail - Safety checks
  • span-evaluator - LLM evaluation

4. Organization

  • projects-python / projects-typescript - Group traces by application
  • sessions-python / sessions-typescript - Track conversations

5. Enrichment

  • metadata-python / metadata-typescript - Custom attributes

6. Production (CRITICAL)

  • production-python / production-typescript - Batch processing, PII masking

7. Feedback

  • annotations-overview - Feedback concepts
  • annotations-python / annotations-typescript - Add feedback to spans

Reference Files

  • fundamentals-overview - Traces, spans, attributes basics
  • fundamentals-required-attributes - Required fields per span type
  • fundamentals-universal-attributes - Common attributes (user.id, session.id)
  • fundamentals-flattening - JSON flattening rules
  • attributes-messages - Chat message format
  • attributes-metadata - Custom metadata schema
  • attributes-graph - Agent workflow attributes
  • attributes-exceptions - Error tracking

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:

# 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
references/attributes-*         # Attribute specifications

# 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:

  • Phoenix Documentation
  • OpenInference Spec

Python API Documentation:

  • Python OTEL Package - arize-phoenix-otel API reference
  • Python Client Package - arize-phoenix-client API reference

TypeScript API Documentation:

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

Score

0–100
65/ 100

Grade

C

Popularity17/30

712 installs — growing adoption. Source repo has 10,134 GitHub stars.

Completeness27/30

Documented: full SKILL.md body, description, one-line install. Missing: category/license metadata.

Trust15/25

Community skill with a public GitHub source repository you can review.

Freshness6/15

No update timestamp is tracked for this skill in our catalog.

Scored automatically from popularity, completeness, trust, and freshness — computed only from data in our catalog, never fabricated.

Proud of your score? Add this badge to your README.

Paste a snippet into your GitHub README. The badge updates automatically and links back to this page.

Phoenix Tracing skill score badge previewScore badge

Markdown

[![Phoenix Tracing skill](https://www.claudemarket.ai/skills/arize-ai/phoenix/phoenix-tracing/badges/score.svg)](https://www.claudemarket.ai/skills/arize-ai/phoenix/phoenix-tracing)

HTML

<a href="https://www.claudemarket.ai/skills/arize-ai/phoenix/phoenix-tracing"><img src="https://www.claudemarket.ai/skills/arize-ai/phoenix/phoenix-tracing/badges/score.svg" alt="Phoenix Tracing skill"/></a>

Phoenix Tracing FAQ

How do I install the Phoenix Tracing skill?

Run “npx skills add https://github.com/arize-ai/phoenix --skill phoenix-tracing” in your terminal. The skill is added to your agent's skills directory and picked up automatically on the next run — no restart or extra configuration needed.

What does the Phoenix Tracing skill do?

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. The full SKILL.md on this page shows the exact instructions the skill gives your agent.

Is the Phoenix Tracing skill free?

Yes. Phoenix Tracing is a free, open-source skill published from arize-ai/phoenix. As with any third-party skill, review the source repository before installing it into an agent with sensitive access.

Does Phoenix Tracing work with Claude Code and OpenClaw?

Yes. Skills use the portable SKILL.md format, so Phoenix Tracing works with Claude Code, OpenClaw, Codex, Hermes, and any other agent that reads SKILL.md skills.

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