Agent skill · software engineering · assistant-ui
observability
Instruments assistant-ui AI SDK backend routes with Langfuse, LangSmith, or Helicone, and renders independent trace data with the experimental @assistant-ui/react-o11y SpanResource and SpanPrimitive APIs. Use it when traces are missing, serverless spans are dropped, an AI SDK call needs provider metadata, Helicone is not proxying a provider, or a trace tree or timeline needs rendering. For streaming transport use streaming, for initial application wiring use setup, for Assistant Cloud persistence use cloud, and for copied styled trace UI use elements.
Why this skill is useful
Adds specific integration patterns and commands for tracing and telemetry in assistant-ui backends that the AI wouldn't reliably generate on its own.
What it needs
Requires @assistant-ui/react-o11y, @langfuse/otel, @opentelemetry/sdk-node installed locally. About 3k tokens when loaded. Last updated 2026-08-02. 20 stars on the source repository.
What this skill does
assistant-ui Observability Always consult assistant-ui.com/llms.txt for the latest API. Observability has two separate layers. Langfuse, LangSmith, and Helicone instrument backend model calls. @assistant-ui/react-o11y renders span data already collected by your application. It does not send traces to a provider or derive spans from the assistant runtime. References ./references/langfuse.md -- OpenTelemetry setup, trace attributes, and serverless flushing for Langfuse. ./references/langsmith.md -- wrapAISDK, metadata, and serverless flushing for LangSmith. ./references/helicone.md -- Helicone proxy setup for AI SDK and OpenAI SDK routes. ./references/react-o11y.md -- SpanResource, every exported span primitive part, span contracts, timeline attributes, and the styled TraceWaterfall option. Choose the backend integration Use one provider based on the data model you need. Langfuse receives the OpenTelemetry spans AI SDK emits when experimentaltelemetry is enabled. Use it for a hierarchical trace across an agent turn, tool calls, and model calls. LangSmith wraps AI SDK functions through wrapAISDK(ai). Use it when traces, evaluations, and prompt management belong with LangChain or LangGraph tooling. Helicone proxies the provider request. Use it for request logs, costs, latency, and prompt diffs without changing the AI SDK call shape. Langfuse and Helicone can run together because one consumes OpenTelemetry spans while the other proxies provider traffic. LangSmith is its own wrapper path, so use its wrapped AI SDK functions instead of the originals. Implementation order 1. Select the provider integration that owns the telemetry destination. 2. Add its server environment variables and startup instrumentation or provider configuration. 3. Instrument one route and verify a real request reaches the provider dashboard. 4. Add serverless flushing before treating traces as reliable in production. 5. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills assistant-ui/observability