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OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Open-source AI observability platform for tracing, evaluating, and improving LLM applications with OpenTelemetry integration
Build and run evaluators for AI/LLM applications using Phoenix.
Design system conventions for the Phoenix frontend — layout, dialogs, error display, BEM CSS class naming, and CSS design tokens. Use when building UI, naming CSS classes, creating or consuming tokens, handling errors, or designing dialog interactions in app/src/.
Frontend development guidelines for the Phoenix AI observability platform. Use when writing, reviewing, or modifying React components, TypeScript code, styles, or UI features in the app/ directory. Triggers on any frontend task — new components, UI changes, styling, accessibility fixes, form handling, or component refactoring. Also use when the user asks about frontend conventions or component patterns for this project. For design system rules (error display, layout, dialogs, tokens), use the phoenix-design skill.
Write Playwright E2E tests for the Phoenix AI observability platform. Use when creating, updating, or debugging Playwright tests, or when the user asks about testing UI features, writing E2E tests, or automating browser interactions for Phoenix.
Development guide for the @arizeai/phoenix-client TypeScript SDK — run and resume experiments, manage OpenTelemetry tracer providers with stack-based attach/detach, and write vitest unit and integration tests. Use when adding features to phoenix-client, debugging experiment lifecycle or provider cleanup, modifying dataset/prompt/session/span APIs, or writing tests for the js/packages/phoenix-client/ directory.
| name | phoenix-tracing |
| description | OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. |
| license | Apache-2.0 |
| compatibility | 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"} |
Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment.
Reference these guidelines when:
| Priority | Category | Description | Prefix |
|---|---|---|---|
| 1 | Setup | Installation and configuration | setup-* |
| 2 | Instrumentation | Auto and manual tracing | instrumentation-* |
| 3 | Span Types | 9 span kinds with attributes | span-* |
| 4 | Organization | Projects and sessions | projects-*, sessions-* |
| 5 | Enrichment | Custom metadata | metadata-* |
| 6 | Production | Batch processing, masking | production-* |
| 7 | Feedback | Annotations and evaluation | annotations-* |
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:
Phoenix Documentation:
Python API Documentation:
arize-phoenix-otel API referencearize-phoenix-client API referenceTypeScript API Documentation:
@arizeai/phoenix-otel, @arizeai/phoenix-client, and other TypeScript packages