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Langfuse production readiness checklist and verification.
Passed 16 of 22 test cases with the skill, and 11 of 22 without it.
and 11 more in the full report
5 fixed11 passed either way6 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 5 test cases the skill fixed. Same model, same prompt, with and without the skill.
Our microservice latency increased because telemetry is sending HTTP batch requests too frequently with small payloads. What specific SDK setting adjustment addresses this overhead?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies small batch size as the cause of latency overhead | Missed | Passed |
| Recommends setting flushAt parameter between 25 and 50 | Missed | Passed |
The instructions your model reads when the skill loads.
Comprehensive checklist for deploying Langfuse observability to production with verified configuration, error handling, graceful shutdown, monitoring, and a pre-deployment verification script.
// v4+ Production Config
import { LangfuseSpanProcessor } from "@langfuse/otel";
import { NodeSDK } from "@opentelemetry/sdk-node";
const processor = new LangfuseSpanProcessor({
exportIntervalMillis: 5000, // Flush every 5s
maxExportBatchSize: 50, // Batch size
maxQueueSize: 2048, // Buffer limit
});
const sdk = new NodeSDK({ spanProcessors: [processor] });
sdk.start();
// Graceful shutdown on all signals
for (const signal of ["SIGTERM", "SIGINT", "SIGUSR2"]) {
process.on(signal, async () => {
await sdk.shutdown();
process.exit(0);
});
}
test cases passed, vs 11 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.