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Skill
/phoenix-arize-setup
Arize Phoenix observability platform setup for LLM debugging and evaluation
Install
$ npx -y skills add a5c-ai/babysitter --skill phoenix-arize-setup --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
- Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
- You can call itInvoke it directly when you want it.
- Slash command
/phoenix-arize-setup
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Arize Phoenix observability platform setup for LLM debugging and evaluation
SKILL.md
phoenix-arize-setup.SKILL.mdname: phoenix-arize-setup
description: Arize Phoenix observability platform setup for LLM debugging and evaluation
allowed-tools:
- Read
- Write
- Edit
- Bash
- Glob
- Grep
graph:
domains: [domain:software-engineering]
specializations: [specialization:ai-agents-conversational]
skillAreas: [skill-area:agent-debugging-logging, skill-area:eval-driven-development]
roles: [role:ml-engineer, role:backend-engineer]
workflows: [workflow:ml-model-lifecycle, workflow:feature-development]
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
Read more
name: phoenix-arize-setup description: Arize Phoenix observability platform setup for LLM debugging and evaluation allowed-tools: - Read - Write - Edit - Bash - Glob - Grep graph: domains: [domain:software-engineering] specializations: [specialization:ai-agents-conversational] skillAreas: [skill-area:agent-debugging-logging, skill-area:eval-driven-development] roles: [role:ml-engineer, role:backend-engineer] workflows: [workflow:ml-model-lifecycle, workflow:feature-development]
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
Ships withbabysitter
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JavaScript
Language
MIT
License
10d ago
Last commit
8mo ago
Created
Repo: a5c-ai/babysitter
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