phoenix-tracing
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
Profile →npx skills add github/awesome-copilot --skill phoenix-tracing --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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-phoe
- When to Apply
- Reference Categories
- Quick Reference
- 1. Setup (START HERE)
- 2. Instrumentation
- 3. Span Types (with full attribute schemas)
- 4. Organization
- 5. Enrichment
- 6. Production (CRITICAL)
- 7. Feedback
- Reference Files
- Common Workflows
- How to Use This Skill
- References
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What does the phoenix-tracing skill do?
OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.
How do I install it?
Run `npx skills add github/awesome-copilot --skill phoenix-tracing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From github/awesome-copilot, a repository with 37,432 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.