customerio-performance-tuning

Optimize Customer.io API performance for high throughput. Use when improving response times, implementing connection pooling, batching, caching, or regional routing. Trigger: "customer.io performance", "optimize customer.io", "customer.io latency", "customer.io connection pooling".

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Repositorio: jeremylongshore/tons-of-skills-marketplace
Última actividad en el origen: 19 de agosto de 2026 a las 12:13
Idioma detectado de SKILL.md: inglés
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Forks: 392

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npx skills add https://github.com/jeremylongshore/tons-of-skills-marketplace --skill customerio-performance-tuning

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langchain-deploy-integration

Deploy a LangChain 1.0 / LangGraph 1.0 app to Cloud Run, Vercel, or LangServe correctly — with timeouts sized for chain length, cold-start mitigation, SSE anti-buffering headers, and Secret Manager over .env. Use when prepping a first production deploy, debugging a stream that hangs behind a proxy, or diagnosing p99 latency spikes. Trigger with "langchain deploy", "langchain cloud run", "langchain vercel python", "langchain langserve", or "langchain docker".

2026-08-26

langchain-langgraph-agents

Build a correct LangGraph 1.0 ReAct agent with create_react_agent — typed tools, error propagation, recursion caps, and stop conditions that actually stop. Use when writing a first tool-calling agent, migrating from AgentExecutor or initialize_agent, or diagnosing an agent that loops on vague prompts. Trigger with "langgraph agent", "create_react_agent", "langgraph tool calling", "AgentExecutor migration", or "agent loop cost".

2026-08-26

langchain-langgraph-human-in-loop

Build LangGraph 1.0 human-in-the-loop approval flows with interrupt_before / interrupt_after and Command(resume=...) — JSON-serializable state, clean resume semantics, and UI wiring for approval decisions. Use when adding an approval gate before an expensive tool call, wiring a Slack/web UI for agent approvals, or debugging a graph that crashes on interrupt. Trigger with "langgraph human in loop", "langgraph interrupt_before", "langgraph approval flow", "Command resume", "langgraph HITL".

2026-08-26

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Customer.io Performance Tuning

Overview

Optimize Customer.io API performance for high-volume integrations: HTTP connection pooling, identify deduplication caching, event batching with flush control, fire-and-forget async tracking, and regional routing.

Prerequisites

Working Customer.io integration

Understanding of your traffic patterns and volume

Monitoring to measure improvement (see customerio-observability)

Performance Targets

Operation

Baseline

Optimized

Technique

Single identify

~200ms

~80ms

Connection pooling

Single track

~200ms

~80ms

Connection pooling

100 events batch

~20s serial

~500ms

Parallel batching

Duplicate identify

~200ms

~0ms

Dedup cache

Non-critical track

Blocking

Non-blocking

Fire-and-forget

Instructions

Step 1: HTTP Connection Pooling

// lib/customerio-pooled.ts import { TrackClient, RegionUS } from "customerio-node"; import https from "https"; // The customerio-node SDK creates new connections by default. // Reuse connections with a keep-alive agent. const agent = new https.Agent({ keepAlive: true, maxSockets: 25, // Max concurrent connections maxFreeSockets: 10, // Keep idle connections open timeout: , : , }); https. = agent; cio = ( process..!, process..!, { : } ); { cio };

name	customerio-performance-tuning
description	Optimize Customer.io API performance for high throughput. Use when improving response times, implementing connection pooling, batching, caching, or regional routing. Trigger: "customer.io performance", "optimize customer.io", "customer.io latency", "customer.io connection pooling".
allowed-tools	Read, Write, Edit, Bash(npm:), Bash(npx:), Glob, Grep
version	1.14.0
license	MIT
author	Jeremy Longshore <jeremy@intentsolutions.io>
tags	["saas","customer-io","performance","optimization"]
compatibility	Designed for Claude Code

Customer.io Performance Tuning

Overview

Prerequisites

Working Customer.io integration
Understanding of your traffic patterns and volume
Monitoring to measure improvement (see customerio-observability)

Performance Targets

Operation	Baseline	Optimized	Technique
Single identify	~200ms	~80ms	Connection pooling
Single track	~200ms	~80ms	Connection pooling
100 events batch	~20s serial	~500ms	Parallel batching
Duplicate identify	~200ms	~0ms	Dedup cache
Non-critical track	Blocking	Non-blocking	Fire-and-forget

Instructions

Step 1: HTTP Connection Pooling

// lib/customerio-pooled.ts
import { TrackClient, RegionUS } from "customerio-node";
import https from "https";

// The customerio-node SDK creates new connections by default.
// Reuse connections with a keep-alive agent.
const agent = new https.Agent({
  keepAlive: true,
  maxSockets: 25,        // Max concurrent connections
  maxFreeSockets: 10,    // Keep idle connections open
  timeout: ,        
  : , 
});




https. = agent;


 cio =  (
  process..!,
  process..!,
  { :  }
);

 { cio };

// lib/customerio-batch.ts import { TrackClient, RegionUS } from "customerio-node"; interface BatchItem { type: "identify" | "track"; userId: string; data: Record<string, any>; } export class CioBatchProcessor { private buffer: BatchItem[] = []; private timer: NodeJS.Timeout | null = null; private client: TrackClient; private processing = false; constructor( private readonly maxBatchSize = 100, private readonly flushIntervalMs = 3000, private readonly concurrency = 15 ) { this.client = new TrackClient( process.env.CUSTOMERIO_SITE_ID!, process.env.CUSTOMERIO_TRACK_API_KEY!, { region: RegionUS } ); this.startFlushTimer(); } add(item: BatchItem): void { this.buffer.push(item); if (this.buffer.length >= this.maxBatchSize) { this.flush(); } } async flush(): Promise<void> { if (this.processing || this.buffer.length === 0) return; this.processing = true; const batch = this.buffer.splice(0, this.maxBatchSize); const startMs = Date.now(); // Process in parallel chunks for (let i = 0; i < batch.length; i += this.concurrency) { const chunk = batch.slice(i, i + this.concurrency); const results = await Promise.allSettled( chunk.map((item) => item.type === "identify" ? this.client.identify(item.userId, item.data) : this.client.track(item.userId, item.data) ) ); const failed = results.filter((r) => r.status === "rejected").length; if (failed > 0) { console.warn(`CIO batch: ${failed}/${chunk.length} failed`); } } const elapsed = Date.now() - startMs; console.log(`CIO batch: ${batch.length} items in ${elapsed}ms`); this.processing = false; } private startFlushTimer(): void { this.timer = setInterval(() => this.flush(), this.flushIntervalMs); } async shutdown(): Promise<void> { if (this.timer) clearInterval(this.timer); await this.flush(); } }

Issue	Solution
High p99 latency	Enable connection pooling, check DNS resolution
Timeout errors	Increase timeout, reduce payload size
Memory growth	Cap LRU cache size, limit batch buffer
Dedup cache misses	Increase TTL if same identify calls are >5min apart

customerio-performance-tuning

Opciones de instalación

Revisa los archivos de origen

Customer.io Performance Tuning

Overview

Prerequisites

Performance Targets

Instructions

Step 1: HTTP Connection Pooling

Customer.io Performance Tuning

Overview

Prerequisites

Performance Targets

Instructions

Step 1: HTTP Connection Pooling

Step 2: Identify Deduplication Cache

Step 3: Batch Processor

Step 4: Fire-and-Forget Async Tracking

Step 5: Regional Routing

Performance Monitoring

Error Handling

Resources

Next Steps

customerio-performance-tuning

Datos de origen

Opciones de instalación

Revisa los archivos de origen

Más de este repositorio

Más de este repositorio

Customer.io Performance Tuning

Overview

Prerequisites

Performance Targets

Instructions

Step 1: HTTP Connection Pooling

Customer.io Performance Tuning

Overview

Prerequisites

Performance Targets

Instructions

Step 1: HTTP Connection Pooling

Step 2: Identify Deduplication Cache

Step 3: Batch Processor

Step 4: Fire-and-Forget Async Tracking

Step 5: Regional Routing

Performance Monitoring

Error Handling

Resources

Next Steps