Wires Langfuse tracing into LLM apps for production observability, monitoring, telemetry, and offline eval - instruments via `@observe` (Python) / `startActiveObservation` (TS) decorators that auto-capture inputs / outputs / timings / errors per generation; exposes `langfuse.update_current_span()` for metadata + cost / latency annotation; supports trace-bound scoring for eval datasets and prompt-as-code management. Use when the user needs to monitor, log, trace, or debug LLM API calls in production beyond pre-deploy eval, wants to add LLM observability tooling to an existing app, or wants to ship traces from production to an eval dataset for offline regression testing.
72
91%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
{
"name": "testland/langfuse-tracing",
"description": "Wires Langfuse tracing into LLM apps for production observability, monitoring, telemetry, and offline eval - instruments via `@observe` (Python) / `startActiveObservation` (TS) decorators that auto-capture inputs / outputs / timings / errors per generation; exposes `langfuse.update_current_span()` for metadata + cost / latency annotation; supports trace-bound scoring for eval datasets and prompt-as-code management. Use when the user needs to monitor, log, trace, or debug LLM API calls in production beyond pre-deploy eval, wants to add LLM observability tooling to an existing app, or wants to ship traces from production to an eval dataset for offline regression testing.",
"private": false,
"skills": [
"."
],
"version": "1.3.6"
}