skilld
All skills
github avatar

/phoenix-tracing

@4214189 official
by githubgithub/awesome-copilot40k stars
5,040

OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.

Ask your Agent

Use this Skill: https://skilld.dev/gh/github/awesome-copilot/phoenix-tracing

This session only. Nothing lands on disk.

referencesspan-evaluator.md

≈420 tokens on demand. Your agent reads this file only when SKILL.md points to it.

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"
}

Source: SKILL.md on GitHub

Third-party checks

No alerts9d4 checks · Risk SAFE
  • Gen Agent Trust Hub9d

    The phoenix-tracing skill provides comprehensive documentation and implementation guides for instrumenting LLM applications with Phoenix and OpenInference semantic conventions. It covers setup, manual and auto-instrumentation, and production best practices for both Python and TypeScript, with a strong focus on secure data handling and PII masking.

  • Socket9d

    No alerts

  • Snyk9d

    Risk: LOW · No issues

  • ZeroLeaks5mo

    Score: 93/100 · 2 sections analyzed

Provenance

Signed by skilld at 4214189. This ties the file your Agent reads to that commit on GitHub. It does not review the instructions.

Last checked against GitHub yesterday.

Activeupdated 2 months ago

Capability

Other metadata
compatibility
Requires Phoenix server. Python skills need arize-phoenix-otel; TypeScript skills need @arizeai/phoenix-otel.
metadata
{
  "author": "oss@arize.com",
  "version": "1.0.0",
  "languages": "Python, TypeScript"
}

Topics

  • Python
  • TypeScript
  • phoenix
  • tracing
  • openinference
  • llm-observability
  • instrumentation
  • otel
  • arize

README badge

README badge for github/awesome-copilot/phoenix-tracing

What it does

Instruments LLM applications with OpenInference tracing in Phoenix, supporting auto-instrumentation of frameworks like OpenAI and LangChain or custom manual spans. Covers setup, span types (LLM calls, retrievers, agents, evaluators), production deployment with PII masking, and feedback annotation in Python and TypeScript.

Generated from the current SKILL.md.

Frequently asked

Does this skill work with both Python and TypeScript?
Yes. The skill provides separate setup, instrumentation, and production guides for both Python (arize-phoenix-otel) and TypeScript (@arizeai/phoenix-otel).
What do I need to install to use this skill?
For Python: arize-phoenix-otel package. For TypeScript: @arizeai/phoenix-otel package. Both require a running Phoenix server endpoint.
Can I auto-instrument frameworks like OpenAI or LangChain?
Yes. The skill includes instrumentation-auto-{lang} references that cover auto-instrumentation for supported frameworks in both languages.
How do I track custom spans for my own operations?
Use the instrumentation-manual-{lang} references to create custom spans with decorators (Python) or wrappers (TypeScript), then refer to span-{type} files for the appropriate attribute schema.
Does this skill cover production deployment concerns?
Yes. The production-{lang} references address batch processing, PII masking, and deployment patterns for production environments.

Generated from the current SKILL.md. These answers refresh after source changes.