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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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Universal Attributes
This document covers attributes that can be used on any span kind in OpenInference.
Overview
These attributes can be used on any span kind to provide additional context, tracking, and metadata.
Input/Output
| Attribute | Type | Description |
|---|---|---|
input.value |
String | Input to the operation (prompt, query, document) |
input.mime_type |
String | MIME type (e.g., "text/plain", "application/json") |
output.value |
String | Output from the operation (response, vector, result) |
output.mime_type |
String | MIME type of output |
Why Capture I/O?
Always capture input/output for evaluation-ready spans:
- Phoenix evaluators (faithfulness, relevance, Q&A correctness) require
input.valueandoutput.value - Phoenix UI displays I/O prominently in trace views for debugging
- Enables exporting I/O for creating fine-tuning datasets
- Provides complete context for analyzing agent behavior
Example attributes:
{
"openinference.span.kind": "CHAIN",
"input.value": "What is the weather?",
"input.mime_type": "text/plain",
"output.value": "I don't have access to weather data.",
"output.mime_type": "text/plain"
}
See language-specific implementation:
- TypeScript:
instrumentation-manual-typescript.md - Python:
instrumentation-manual-python.md
Session and User Tracking
| Attribute | Type | Description |
|---|---|---|
session.id |
String | Session identifier for grouping related traces |
user.id |
String | User identifier for per-user analysis |
Example:
{
"openinference.span.kind": "LLM",
"session.id": "session_abc123",
"user.id": "user_xyz789"
}
Metadata
| Attribute | Type | Description |
|---|---|---|
metadata |
string | JSON-serialized object of key-value pairs |
Example:
{
"openinference.span.kind": "LLM",
"metadata": "{\"environment\": \"production\", \"model_version\": \"v2.1\", \"cost_center\": \"engineering\"}"
}