Phoenix Tracing SkillCAUTION
AI Observability & Evaluation
Overview
From the repository's own README, as read at the audited commit. Badges and raw HTML are left out.
OpenInference semantic conventions and instrumentation guides for Phoenix.
Usage
Start with SKILL.md for the index and quick reference.
File Organization
All files in flat references/ directory with semantic prefixes:
span-*- Span kinds (LLM, CHAIN, TOOL, etc.)setup-*,instrumentation-*- Getting started guidesfundamentals-*,attributes-*- Reference docsannotations-*,export-*- Advanced features
Reference
1806650f7d1dOBSERVED · 2026-10-22Install
Commands as the repository documents them. They are shown, not run.
pip install arize-phoenix-client
pip install arize-phoenix-otel
pip install openinference-instrumentation-openai # Add others as needed
pip install opentelemetry-instrumentation-anthropic
pip install arize-phoenix-otel
npm install @arizeai/phoenix-otel @arizeai/openinference-core
Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| claude-code | mentioned | |
| codex | mentioned | |
| gemini-cli | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- 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. (also covers OTel GenAI native instrumentation) - [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 - [instrumentation-atif-python](references/instrumentation-atif-python.md) - Import ATIF agent trajectories (Claude Code, OpenHands, Codex, etc.) ### 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 - [span-decision](references/span-decision.md) - Decision model calls (routing, classification, scoring) — experimental ### 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 # 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-*
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (5)
.claude/skills
.codex/skills
.cursor/skills/mintlify
.cursor/skills/vercel-react-best-practices
CLAUDE.md
Gates applied: no_behavioural_pass.
1806650f7d1dfull audit observations/trust-audit/skill/arize-ai__phoenix-tracing.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-22 | 1806650f7d1d | CAUTION | B | 89 | first audit |
Questions
What does the phoenix-tracing — arize-ai/phoenix skill do?
AI Observability & Evaluation
Is phoenix-tracing — arize-ai/phoenix safe to install?
With care. The audit graded it B (89/100) and found 5 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can phoenix-tracing — arize-ai/phoenix access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does phoenix-tracing — arize-ai/phoenix work with?
Its documentation mentions claude-code, codex and gemini-cli. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (1806650f7d1d), read on 2026-10-22. The repository is watched, and a new audit runs when it changes — this is the first audit.
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