← LeaderboardThe Skill Leaderboard

phoenix-tracing

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

Published by github ↗github/awesome-copilot

Try your first task

phoenix-tracing

Start a fresh conversation. Replace the highlighted placeholders.

Use phoenix-tracing to instrument [LLM application] with OpenInference traces for [workflow], including the applicable LLM, retrieval, tool, or agent spans; Phoenix server access is required.

Required inputs

  • [LLM application]
  • [workflow]

User-approved starter prompt based on a pinned source version; check current skill requirements before running. Task evidence ↗

SKILL.md

name: phoenix-tracingdescription: OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.license: Apache-2.0compatibility: Requires Phoenix server. Python skills need arize-phoenix-otel; TypeScript skills need @arizeai/phoenix-otel.metadata:   author: oss@arize.com  version: "1.0.0"  languages: "Python, TypeScript"

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:

# 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:

Python API Documentation:

TypeScript API Documentation:

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