Agent skill · data analytics · github
phoenix-tracing
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Why this skill is useful
Provides domain-specific knowledge on OpenInference tracing conventions and instrumentation for LLM applications that the AI wouldn't reliably generate on its own.
What it needs
Requires @arizeai/phoenix-otel, arize-phoenix-otel installed locally. About 2k tokens when loaded. Last updated 2026-08-07. 37,534 stars on the source repository.
What this skill does
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 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- 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. instrumentation-auto-typescript - Auto-instrument supported frameworks instrumentation-manual-python - Custom spans with decorators instrumentation-manual-typescript - Custom spans with wrappers 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills github/phoenix-tracing