chore: publish from staged

plugins/phoenix/skills/phoenix-tracing/references/fundamentals-required-attributes.md

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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:**
+
+```json
+{
+  "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:**
+```json
+{
+  "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 |