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Jim Bennett d79183139a Add Arize and Phoenix LLM observability skills (#1204 )

* 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>

2026-04-02 09:58:55 +11:00

1.6 KiB

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EVALUATOR Spans

Purpose

EVALUATOR spans represent quality assessment operations (answer relevance, faithfulness, hallucination detection).

Required Attributes

Attribute	Type	Description	Required
`openinference.span.kind`	String	Must be "EVALUATOR"	Yes

Common Attributes

Attribute	Type	Description
`input.value`	String	Content being evaluated
`output.value`	String	Evaluation result (score, label, explanation)
`metadata.evaluator_name`	String	Evaluator identifier
`metadata.score`	Float	Numeric score (0-1)
`metadata.label`	String	Categorical label (relevant/irrelevant)

Example: Answer Relevance

{
  "openinference.span.kind": "EVALUATOR",
  "input.value": "{\"question\": \"What is the capital of France?\", \"answer\": \"The capital of France is Paris.\"}",
  "input.mime_type": "application/json",
  "output.value": "0.95",
  "metadata.evaluator_name": "answer_relevance",
  "metadata.score": 0.95,
  "metadata.label": "relevant",
  "metadata.explanation": "Answer directly addresses the question with correct information"
}

Example: Faithfulness Check

{
  "openinference.span.kind": "EVALUATOR",
  "input.value": "{\"context\": \"Paris is in France.\", \"answer\": \"Paris is the capital of France.\"}",
  "input.mime_type": "application/json",
  "output.value": "0.5",
  "metadata.evaluator_name": "faithfulness",
  "metadata.score": 0.5,
  "metadata.label": "partially_faithful",
  "metadata.explanation": "Answer makes unsupported claim about Paris being the capital"
}

1.6 KiB Raw Blame History