documenso-observability

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Implement monitoring, logging, and tracing for Documenso integrations. Use when setting up observability, implementing metrics collection, or debugging production issues. Trigger with phrases like "documenso monitoring", "documenso metrics", "documenso logging", "documenso tracing", "documenso observability".

AI & Automation 2,359 stars 334 forks Updated today MIT

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# Documenso Observability ## Overview Implement monitoring, structured logging, and health checks for Documenso integrations. Since Documenso does not expose rate limit headers or usage metrics via API, observability is built around your API call patterns, latency, error rates, and webhook delivery. ## Prerequisites - Working Documenso integration - Monitoring stack (Prometheus/Grafana, Datadog, or CloudWatch) - Logging infrastructure ## Instructions ### Step 1: Instrumented Client Wrapper ```typescript // src/observability/documenso-metrics.ts import { Documenso } from "@documenso/sdk-typescript"; interface Metrics { requestCount: number; errorCount: number; totalLatencyMs: number; errorsByStatus: Record<number, number>; } const metrics: Metrics = { requestCount: 0, errorCount: 0, totalLatencyMs: 0, errorsByStatus: {}, }; export function createInstrumentedClient(): Documenso { const client = new Documenso({ apiKey: process.env.DOCUMENSO_API_KEY! }); return new Proxy(client, { get(target, prop) { const value = (target as any)[prop]; if (typeof value === "object" && value !== null) { return new Proxy(value, { get(innerTarget, method) { const fn = (innerTarget as any)[method]; if (typeof fn !== "function") return fn; return async (...args: any[]) => { const start = Date.now(); metrics.requestCount++; try { const result ...

Details

Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
8 months ago
Last Updated
today
Language
Python
License
MIT

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