arize-prompt-optimization
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
Works with GitHubLow risk
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- author
@github- language
- Python
★30kstars
3.6kforks
Apr 18, 2026
name
arize-prompt-optimization
description
INVOKE THIS SKILL when optimizing, improving, or debugging LLM prompts using production trace data, evaluations, and annotations. Covers extracting prompts from spans, gathering performance signal, and running a data-driven optimization loop using the ax CLI.
Arize Prompt Optimization Skill
Concepts
Where Prompts Live in Trace Data
LLM applications emit spans following OpenInference semantic conventions. Prompts are stored in different span attributes depending on the span kind and instrumentation:
| Column | What it contains | When to use |
|---|---|---|
attributes.llm.input_messages | Structured chat messages (system, user, assistant, tool) in role-based format | Primary source for chat-based LLM prompts |
attributes.llm.input_messages.roles | Array of roles: system, user, assistant, tool | Extract individual message roles |
attributes.llm.input_messages.contents | Array of message content strings | Extract message text |
attributes.input.value | Serialized prompt or user question (generic, all span kinds) | Fallback when structured messages are not available |
attributes.llm.prompt_template.template | Template with {variable} placeholders (e.g., "Answer {question} using {context}") | When the app uses prompt templates |
$install
1-click copynpx skills add github/awesome-copilot --skill arize-prompt-optimizationSafety assessment
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