mirror of
https://github.com/github/awesome-copilot.git
synced 2026-08-16 22:26:56 +00:00
Stephen Benjamin
4214189287
While implementing the Agent Plugin v1 spec in https://github.com/stbenjam/skillsaw/pull/483, I used this repository as a canonical real-world source for tests. Along the way its checks surfaced genuine correctness issues worth fixing regardless of tooling: - Internal links that do not resolve (22): wrong relative paths, links whose targets are runtime output paths rather than bundled files (now inline code), a schemeless external URL parsed as a relative path, and links to files that do not exist (removed). - Bundled skill files never referenced from their SKILL.md (14): each is now cited from the section where it is used, so nothing ships unreachable from the skill's instructions. - Unclosed or mis-nested code fences (11): markdown templates embedding code blocks now use four-backtick outer fences so inner fences nest instead of closing the template early and leaking example content as live markdown; also removed stray orphan fences and closed an unterminated block at end of file. - Invisible unicode (1): removed a zero-width space from a code example, a copy-paste artifact invisible to reviewers.
6.3 KiB
6.3 KiB
name, description, license, compatibility, metadata
| name | description | license | compatibility | metadata | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| phoenix-tracing | OpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production. | Apache-2.0 | Requires Phoenix server. Python skills need arize-phoenix-otel; TypeScript skills need @arizeai/phoenix-otel. |
|
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 - Install arize-phoenix-otel, configure endpoint
- setup-typescript - Install @arizeai/phoenix-otel, configure endpoint
2. Instrumentation
- instrumentation-auto-python - Auto-instrument OpenAI, LangChain, etc.
- instrumentation-auto-typescript - Auto-instrument supported frameworks
- instrumentation-manual-python - Custom spans with decorators
- instrumentation-manual-typescript - Custom spans with wrappers
3. Span Types (with full attribute schemas)
- span-llm - LLM API calls (model, tokens, messages, cost)
- span-chain - Multi-step workflows and pipelines
- span-retriever - Document retrieval (documents, scores)
- span-tool - Function/API calls (name, parameters)
- span-agent - Multi-step reasoning agents
- span-embedding - Vector generation
- span-reranker - Document re-ranking
- span-guardrail - Safety checks
- span-evaluator - LLM evaluation
4. Organization
- projects-python / projects-typescript - Group traces by application
- sessions-python / sessions-typescript - Track conversations
5. Enrichment
- metadata-python / metadata-typescript - Custom attributes
6. Production (CRITICAL)
- production-python / production-typescript - Batch processing, PII masking
7. Feedback
- annotations-overview - Feedback concepts
- annotations-python / annotations-typescript - Add feedback to spans
Reference Files
- fundamentals-overview - Traces, spans, attributes basics
- fundamentals-required-attributes - Required fields per span type
- fundamentals-universal-attributes - Common attributes (user.id, session.id)
- fundamentals-flattening - 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:
# 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:
- Start with setup-{lang} for your language
- Choose instrumentation-auto-{lang} OR instrumentation-manual-{lang}
- Reference span-{type} files as needed for specific operations
- See fundamentals-* files for attribute specifications
References
Phoenix Documentation:
Python API Documentation:
- Python OTEL Package -
arize-phoenix-otelAPI reference - Python Client Package -
arize-phoenix-clientAPI reference
TypeScript API Documentation:
- TypeScript Packages -
@arizeai/phoenix-otel,@arizeai/phoenix-client, and other TypeScript packages