phoenix-arize-setup
Arize Phoenix observability platform setup for LLM debugging and evaluation
Best use case
phoenix-arize-setup is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Arize Phoenix observability platform setup for LLM debugging and evaluation
Teams using phoenix-arize-setup should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/phoenix-arize-setup/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How phoenix-arize-setup Compares
| Feature / Agent | phoenix-arize-setup | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Arize Phoenix observability platform setup for LLM debugging and evaluation
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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
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