Skill UI

Production-grade Langfuse architecture patterns for building scalable LLM observability, covering singleton SDK setup, trace context propagation, queue-based ingestion, multi-environment configuration, and service mesh tracing so teams can design reliable tracing pipelines.

Guides teams through Langfuse security best practices, covering API key handling, PII scrubbing before tracing, access control on self-hosted deployments, and retention cleanup to keep LLM observability data private.

Guides adding Arize AX tracing to apps via an agent-assisted two-phase flow, emphasizing read-only analysis before manual instrumentation, favoring auto-integration, and adding manual chain/tool spans for LLM tool calls.