phoenix-tracing — GitHub Copilot skill for Phoenix tracing
This skill guides OpenInference tracing for LLM apps with Phoenix. Use it to set up instrumentation, define custom spans, or prepare tracing for production.
npx skills add github/awesome-copilot --skill phoenix-tracingWhat phoenix-tracing does
The reference material covers setup for Python and TypeScript, automatic and manual instrumentation, span kinds, and their attributes. It also includes guidance on grouping traces into projects and sessions, adding metadata, batching data, masking PII, and annotating spans with feedback.
Start with setup-python or setup-typescript, then choose automatic or manual instrumentation. A Phoenix server is required. Python projects need arize-phoenix-otel; TypeScript projects need @arizeai/phoenix-otel.
Install it when you are adding tracing or need to follow OpenInference conventions. Skip it if your project does not use Phoenix.
What it helps with
- configure a Phoenix endpoint for a Python or TypeScript app
- add automatic tracing for a supported framework
- create custom spans for LLM operations
- group traces by project or conversation session
- prepare batching and PII masking for production
Example prompts for the agent
- Set up Phoenix tracing in my Python app and add automatic OpenAI instrumentation.
- Create custom spans for each step in this LLM pipeline using OpenInference conventions.
- How should I group conversation traces into sessions and attach feedback to spans?
How to install phoenix-tracing
1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill phoenix-tracing2GitHub CLI
gh skill install github/awesome-copilot phoenix-tracing3Manually: put the files in place
.github/skills/phoenix-tracing/SKILL.md1Skills CLI (Vercel)
npx skills add github/awesome-copilot --skill phoenix-tracing -a claude-code2Manually: put the files in place
~/.claude/skills/phoenix-tracing/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/phoenix-tracing/
Manually: put the files in place
.agents/skills/phoenix-tracing/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/phoenix-tracing/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
Platform CLI
gemini skills install https://github.com/github/awesome-copilot --path skills/phoenix-tracingManually: put the files in place
.devin/skills/phoenix-tracing/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
From the same set554
The whole set →Similar
Questions about phoenix-tracing
How do I install phoenix-tracing in GitHub Copilot?
Run npx skills add github/awesome-copilot --skill phoenix-tracing or gh skill install github/awesome-copilot phoenix-tracing.
What does phoenix-tracing do?
It provides references for instrumenting LLM applications with OpenInference tracing in Phoenix, covering setup, instrumentation, span types, and production workflows.
What does phoenix-tracing require?
It requires a Phoenix server. Python projects also need arize-phoenix-otel; TypeScript projects need @arizeai/phoenix-otel.
Source text
SKILL.md · Apache-2.0
--- name: phoenix-tracing description: OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. license: Apache-2.0 compatibility: Requires Phoenix server. Python skills need arize-phoenix-otel; TypeScript skills need @arizeai/phoenix-otel. metadata: author: [email protected] version: "1.0.0" languages: "Python, TypeScript" --- # Phoenix Tracing Comprehensive guide for instrumenting LLM applications with OpenInference tracing in Phoenix. Contains reference files covering setup, instrumentation, span types, and production deployment. ## When to Apply Reference these guidelines when: - Setting up Phoenix tracing (Python or TypeScript) - Creating custom spans for LLM operations - Adding attributes following OpenInference conventions - Deploying tracing to production - Querying and analyzing trace data ## Reference Categories | Priority | Category | Description | Prefix | | -------- | --------------- | ------------------------------ | -------------------------- | | 1 | Setup | Installation and configuration | `setup-*` | | 2 | Instrumentation | Auto and manual tracing | `instrumentation-*` | | 3 | Span Types | 9 span kinds with attributes | `span-*` | | 4 | Organization | Projects and sessions | `projects-*`, `sessions-*` | | 5 | Enrichment | Custom metadata | `metadata-*` | | 6 | Production | Batch processing, masking | `production-*` | | 7 | Feedback | Annotations and evaluation | `annotations-*` | ## Quick Reference ### 1. Setup (START HERE) - [setup-python](references/setup-python.md) - Install arize-phoenix-otel, configure endpoint - [setup-typescript](references/setup-typescript.md) - Install @arizeai/phoenix-otel, configure endpoint ### 2. Instrumentation - [instrumentation-auto-python](references/instrumentation-auto-python.md) - Auto-instrument OpenAI, LangChain, etc. - [instrumentation-auto-typescript](references/instrumentation-auto-typescript.md) - Auto-instrument supported frameworks - [instrumentation-manual-python](references/instrumentation-manual-python.md) - Custom spans with decorators - [instrumentation-manual-typescript](references/instrumentation-manual-typescript.md) - Custom spans with wrappers ### 3. Span Types (with full attribute schemas) - [span-llm](references/span-llm.md) - LLM API calls (model, tokens, messages, cost) - [span-chain](references/span-chain.md) - Multi-step workflows and pipelines - [span-retriever](references/span-retriever.md) - Document retrieval (documents, scores) - [span-tool](references/span-tool.md) - Function/API calls (name, parameters) - [span-agent](references/span-agent.md) - Multi-step reasoning agents - [span-embedding](references/span-embedding.md) - Vector generation - [span-reranker](references/span-reranker.md) - Document re-ranking - [span-guardrail](references/span-guardrail.md) - Safety checks - [span-evaluator](references/span-evaluator.md) - LLM evaluation ### 4. Organization - [projects-python](references/projects-python.md) / [projects-typescript](references/projects-typescript.md) - Group traces by application - [sessions-python](references/sessions-python.md) / [sessions-typescript](references/sessions-typescript.md) - Track conversations ### 5. Enrichment - [metadata-python](references/metadata-python.md) / [metadata-typescript](references/metadata-typescript.md) - Custom attributes ### 6. Production (CRITICAL) - [production-python](references/production-python.md) / [production-typescript](references/production-typescript.md) - Batch processing, PII masking ### 7. Feedback - [annotations-overview](references/annotations-overview.md) - Feedback concepts - [annotations-python](references/annotations-python.md) / [annotations-typescript](references/annotations-typescript.md) - Add feedback to spans ### Reference Files - [fundamentals-overview](references/fundamentals-overview.md) - Traces, spans, attributes basics - [fundamentals-required-attributes](references/fundamentals-required-attributes.md) - Required fields per span type - [fundamentals-universal-attributes](references/fundamentals-universal-attributes.md) - Common attributes (user.id, session.id) - [fundamentals-flattening](references/fundamentals-flattening.md) - JSON flattening rules ## Common Workflows - **Quick Start**: setup-{lang} → instrumentation-auto-{lang} → Check Phoenix - **Custom Spans**: setup-{lang} → instrumentation-manual-{lang} → span-{type} - **Session Tracking**: sessions-{lang} for conversation grouping patterns - **Production**: production-{lang} for batching, masking, and deployment ## How to Use This Skill **Navigation Patterns:** ```bash # By category prefix references/setup-* # Installation and configuration references/instrumentation-* # Auto and manual tracing references/span-* # Span type specifications references/sessions-* # Session tracking references/production-* # Production deployment references/fundamentals-* # Core concepts references/attributes-* # Attribute specifications # By language references/*-python.md # Python implementations references/*-typescript.md # TypeScript implementations ``` **Reading Order:** 1. Start with setup-{lang} for your language 2. Choose instrumentation-auto-{lang} OR instrumentation-manual-{lang} 3. Reference span-{type} files as needed for specific operations 4. See fundamentals-* files for attribute specifications ## References **Phoenix Documentation:** - [Phoenix Documentation](https://docs.arize.com/phoenix) - [OpenInference Spec](https://github.com/Arize-ai/openinference/tree/main/spec) **Python API Documentation:** - [Python OTEL Package](https://arize-phoenix.readthedocs.io/projects/otel/en/latest/) - `arize-phoenix-otel` API reference - [Python Client Package](https://arize-phoenix.readthedocs.io/projects/client/en/latest/) - `arize-phoenix-client` API reference **TypeScript API Documentation:** - [TypeScript Packages](https://arize-ai.github.io/phoenix/) - `@arizeai/phoenix-otel`, `@arizeai/phoenix-client`, and other TypeScript packages
© [email protected] · Apache-2.0 · github/awesome-copilot