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* Add 9 Arize LLM observability skills Add skills for Arize AI platform covering trace export, instrumentation, datasets, experiments, evaluators, AI provider integrations, annotations, prompt optimization, and deep linking to the Arize UI. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add 3 Phoenix AI observability skills Add skills for Phoenix (Arize open-source) covering CLI debugging, LLM evaluation workflows, and OpenInference tracing/instrumentation. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Ignoring intentional bad spelling * Fix CI: remove .DS_Store from generated skills README and add codespell ignore Remove .DS_Store artifact from winmd-api-search asset listing in generated README.skills.md so it matches the CI Linux build output. Add queston to codespell ignore list (intentional misspelling example in arize-dataset skill). Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Add arize-ax and phoenix plugins Bundle the 9 Arize skills into an arize-ax plugin and the 3 Phoenix skills into a phoenix plugin for easier installation as single packages. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fix skill folder structures to match source repos Move arize supporting files from references/ to root level and rename phoenix references/ to rules/ to exactly match the original source repository folder structures. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> * Fixing file locations * Fixing readme --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Required and Recommended Attributes
This document covers the required attribute and highly recommended attributes for all OpenInference spans.
Required Attribute
Every span MUST have exactly one required attribute:
{
"openinference.span.kind": "LLM"
}
Highly Recommended Attributes
While not strictly required, these attributes are highly recommended on all spans as they:
- Enable evaluation and quality assessment
- Help understand information flow through your application
- Make traces more useful for debugging
Input/Output Values
| Attribute | Type | Description |
|---|---|---|
input.value |
String | Input to the operation (prompt, query, document) |
output.value |
String | Output from the operation (response, result, answer) |
Example:
{
"openinference.span.kind": "LLM",
"input.value": "What is the capital of France?",
"output.value": "The capital of France is Paris."
}
Why these matter:
- Evaluations: Many evaluators (faithfulness, relevance, hallucination detection) require both input and output to assess quality
- Information flow: Seeing inputs/outputs makes it easy to trace how data transforms through your application
- Debugging: When something goes wrong, having the actual input/output makes root cause analysis much faster
- Analytics: Enables pattern analysis across similar inputs or outputs
Phoenix Behavior:
- Input/output displayed prominently in span details
- Evaluators can automatically access these values
- Search/filter traces by input or output content
- Export inputs/outputs for fine-tuning datasets
Valid Span Kinds
There are exactly 9 valid span kinds in OpenInference:
| Span Kind | Purpose | Common Use Case |
|---|---|---|
LLM |
Language model inference | OpenAI, Anthropic, local LLM calls |
EMBEDDING |
Vector generation | Text-to-vector conversion |
CHAIN |
Application flow orchestration | LangChain chains, custom workflows |
RETRIEVER |
Document/context retrieval | Vector DB queries, semantic search |
RERANKER |
Result reordering | Rerank retrieved documents |
TOOL |
External tool invocation | API calls, function execution |
AGENT |
Autonomous reasoning | ReAct agents, planning loops |
GUARDRAIL |
Safety/policy checks | Content moderation, PII detection |
EVALUATOR |
Quality assessment | Answer relevance, faithfulness scoring |