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Langfuse SDK best practices, patterns, and idiomatic usage.
Passed 20 of 22 test cases with the skill, and 11 of 22 without it.
and 8 more in the full report
9 fixed11 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
In @langfuse/tracing, what utility function is invoked inside an observe-wrapped function body to dynamically attach input arguments and output results to the active span?
| Checked | Without skill | With skill |
|---|---|---|
| Calls updateActiveObservation to set input and output fields | Missed | Passed |
The instructions your model reads when the skill loads.
Production-quality patterns for the Langfuse SDK: singleton clients, the observe wrapper, startActiveObservation for nested traces, session tracking, graceful shutdown, and error-safe tracing.
langfuse-install-auth setup@langfuse/tracing, @langfuse/otel, @opentelemetry/sdk-node// src/lib/langfuse.ts -- single file, import everywhere
import { LangfuseClient } from "@langfuse/client";
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
// Singleton client for prompts, datasets, scores
let client: LangfuseClient | null = null;
export function getLangfuseClient(): LangfuseClient {
if (!client) {
client = new LangfuseClient();
}
return client;
}
// One-time OTel setup (call at app entry point)
let sdk: NodeSDK | null = null;
export function initTracing(): NodeSDK {
if (!sdk) {
sdk = new NodeSDK({
spanProcessors: [new LangfuseSpanProcessor()],
});
sdk.start();
test cases passed, vs 11 / 22 without
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