Phoenix Arize Setup Skill

SkillMonitoring & ops

Arize Phoenix observability platform setup for LLM debugging and evaluation

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Details

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

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Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Phoenix Arize Setup SkillStart free

What this skill tells your AI

The instructions your AI receives, as published by a5c-ai/babysitter in library/specializations/ai-agents-conversational/skills/phoenix-arize-setup/SKILL.md and read by ahel’s review.

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

Tracing: OpenTelemetry-based LLM traces
Evals: LLM-as-judge evaluations
Embeddings: Visualization and drift detection
Retrieval: RAG quality analysis
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

Signals

GitHub stars: 2k
Forks: 112
Last commit: Sep 2026

Advanced

Item type: skill
Key: phoenix-arize-setup
Source: github.com/a5c-ai/babysitter

github.com/a5c-ai/babysitter