phoenix-arize-setup
Arize Phoenix observability platform setup for LLM debugging and evaluation
npx skills add majiayu000/claude-skill-registry --skill phoenix-arize-setup --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Phoenix Arize Setup Skill ## Capabilities - Set up Phoenix local server - Configure tracing instrumentation - Design evaluation experiments - Implement embedding visualizations - Set up retrieval analysis - Create custom evaluations with LLM-as-judge ## Target Processes - llm-observability-monitoring - agent-evaluation-framework ## Implementation Details ### Core Features 1. **Tracing**: OpenTelemetry-based LLM traces 2. **Evals**: LLM-as-judge evaluations 3. **Embeddings**: Visualization and drift detection 4. **Retrieval**: RAG quality analysis 5. **Datasets**: Experiment management ### Instrumentation - OpenAI auto-instrumentation - LangChain instrumentation - LlamaIndex instrumentation - Custom span creation ### Configuration Options - Phoenix server setup - Trace sampling - Evaluation metrics - Embedding models - Export settings ### Best Practices - Comprehensive instrumentation - Regular evaluation runs - Monitor embedding drift - Analyze retrieval quality ### Dependencies - arize-phoenix - openinference-instrumentation-openai
- Capabilities
- Target Processes
- Implementation Details
- Core Features
- Instrumentation
- Configuration Options
- Best Practices
- Dependencies
What does the phoenix-arize-setup skill do?
Arize Phoenix observability platform setup for LLM debugging and evaluation
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill phoenix-arize-setup --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.