phoenix-observability
De la autor
Open-source AI observability platform for LLM tracing, evaluation, and monitoring. Use when debugging LLM applications with detailed traces, running evaluations on datasets, or monitoring production AI systems with real-time insights.
Instalare
npx skills add Orchestra-Research/AI-Research-SKILLs --skill phoenix-observability -a claude-code
Se instalează în .claude/skills/phoenix-observability/ · Fiecare comandă descarcă skill-ul direct de pe GitHub cu utilitarul open source skills.
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Skill-uri similare
Cele mai apropiate de acesta prin ce fac și când sunt folosite.
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- phoenix-tracinggithub/awesome-copilotOpenInference semantic conventions and instrumentation for Phoenix AI observability. Use when implementing LLM tracing, creating custom spans, or deploying to production.DevOps★ 40.3K
- langfusesickn33/agentic-awesome-skillsExpert in Langfuse - the open-source LLM observability platform. Covers tracing, prompt management, evaluation, datasets, and integration with LangChain, LlamaIndex, and OpenAI. Essential for debugging, monitoring, and improving LLM applications in production.DevOps★ 48.2K
- python-observabilitywshobson/agentsPython observability patterns including structured logging, metrics, and distributed tracing. Use when adding logging, implementing metrics collection, setting up tracing, or debugging production systems.DevOps★ 41.4K
- service-mesh-observabilitywshobson/agentsImplement comprehensive observability for service meshes including distributed tracing, metrics, and visualization. Use when setting up mesh monitoring, debugging latency issues, or implementing SLOs for service communication.DevOps★ 41.4K
- phoenix-cligithub/awesome-copilotDebug LLM applications using the Phoenix CLI. Fetch traces, analyze errors, structure trace review with open coding and axial coding, inspect datasets, review experiments, query annotation configs, and use the GraphQL API. Use whenever the user is analyzing traces or spans, investigating LLM/agent failures, deciding what to do after instrumenting an app, building failure taxonomies, choosing what evals to write, or asking "what's going wrong", "what kinds of mistakes", or "where do I focus" — even without naming a technique.Inginerie★ 40.3K
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