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Agent Skill · 前端设计

customerio-performance-tuning(客户性能调整)

customerio-performance-tuning 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

customerio-performance-tuning 是前端设计类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在前端设计类的 10,216 个 Skill 里排第 5,948。

总安装 574周安装 23GitHub Stars 2,125下载 186核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:customerio-performance-tuning(客户性能调整)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/customerio-performance-tuning
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill customerio-performance-tuning
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill customerio-performance-tuning

这个 Skill 是做什么的

前端设计类 Skill 关注页面结构、视觉规范和组件写法,输出通常是可运行的界面代码。

优化高并发下的 API 性能,通过连接池、缓存和批量处理降低延迟。支持 identify 去重缓存、事件批处理与异步跟踪,显著提升吞吐量表现。提供 HTTP 连接复用、区域路由和熔断机制等企业级调优手段。调优前需建立基线性能指标,并通过监控工具持续验证改进效果。

customerio-performance-tuning 属于前端设计类 Skill,可作为该场景下的辅助能力补充。

来源仓库内路径:skills/customerio-performance-tuning

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skill…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

customerio-performance-tuning 怎么安装?

在终端执行 npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

customerio-performance-tuning 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

customerio-performance-tuning 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

customerio-performance-tuning 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

customerio-performance-tuning 用的人多吗?

公开数据是累计安装 574 次,近一周安装 23 次,下载 186 次,GitHub 2,125 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

customerio-performance-tuning 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 2,125 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理前端设计类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

前端设计类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
frontend-design

前端设计 · Codex、Claude

8,823,360126,072只读MIT通过
vercel-composition-patterns

Vercel 组合模式 · Codex、Claude

3,858,81625,893需要联网MIT通过
vercel-react-native-skills

Vercel React native skills 前端 · Codex、Claude

2,468,66425,878只读MIT通过
adapt

adapt 搜索 · Codex、Claude

2,027,04023,125操作浏览器未明确通过
bolder

bolder 分析 · Codex、Claude

2,004,28823,130只读未明确通过
customerio-performance-tuning

客户性能调整 · 本页

5742,125需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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

OperationBaselineOptimizedTechnique
Single identify~200ms~80msConnection pooling
Single track~200ms~80msConnection pooling
100 events batch~20s serial~500msParallel batching
Duplicate identify~200ms~0msDedup cache
Non-critical trackBlockingNon-blockingFire-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: 30000,        // 30s socket timeout
  keepAliveMsecs: 15000, // TCP keep-alive probe interval
});

// Apply to the SDK by creating a singleton with the agent
// Note: customerio-node doesn't directly accept an agent,
// but we configure Node.js global agent for HTTPS
https.globalAgent = agent;

// Singleton client — one instance = one connection pool
const cio = new TrackClient(
  process.env.CUSTOMERIO_SITE_ID!,
  process.env.CUSTOMERIO_TRACK_API_KEY!,
  { region: RegionUS }
);

export { cio };

Step 2: Identify Deduplication Cache

// lib/customerio-dedup.ts
// Skip duplicate identify() calls within a time window

class LRUCache<K, V> {
  private map = new Map<K, V>();
  constructor(private maxSize: number) {}

  get(key: K): V | undefined {
    const val = this.map.get(key);
    if (val !== undefined) {
      // Move to end (most recent)
      this.map.delete(key);
      this.map.set(key, val);
    }
    return val;
  }

  set(key: K, val: V): void {
    this.map.delete(key);
    this.map.set(key, val);
    if (this.map.size > this.maxSize) {
      const oldest = this.map.keys().next().value;
      this.map.delete(oldest!);
    }
  }
}

import { createHash } from "crypto";
import { TrackClient, RegionUS } from "customerio-node";

const identifyCache = new LRUCache<string, number>(10_000);
const DEDUP_TTL_MS = 5 * 60 * 1000;  // 5 minutes

const cio = new TrackClient(
  process.env.CUSTOMERIO_SITE_ID!,
  process.env.CUSTOMERIO_TRACK_API_KEY!,
  { region: RegionUS }
);

export async function dedupIdentify(
  userId: string,
  attrs: Record<string, any>
): Promise<void> {
  // Create a hash of userId + attributes
  const hash = createHash("sha256")
    .update(userId + JSON.stringify(attrs))
    .digest("hex")
    .substring(0, 16);

  const cached = identifyCache.get(hash);
  if (cached && Date.now() - cached < DEDUP_TTL_MS) {
    return; // Skip — identical identify() call within TTL window
  }

  await cio.identify(userId, attrs);
  identifyCache.set(hash, Date.now());
}

Step 3: Batch Processor

// 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();
  }
}

Step 4: Fire-and-Forget Async Tracking

// lib/customerio-async.ts
// For non-critical analytics events — don't block the request path

import { TrackClient, RegionUS } from "customerio-node";

const cio = new TrackClient(
  process.env.CUSTOMERIO_SITE_ID!,
  process.env.CUSTOMERIO_TRACK_API_KEY!,
  { region: RegionUS }
);

export function fireAndForgetTrack(
  userId: string,
  eventName: string,
  data?: Record<string, any>
): void {
  // No await — returns immediately
  cio
    .track(userId, { name: eventName, data })
    .catch((err) => console.error(`CIO async track failed: ${err.message}`));
}

// Usage in Express route — does NOT slow down response
router.get("/dashboard", async (req, res) => {
  fireAndForgetTrack(req.user.id, "dashboard_viewed", {
    timestamp: Math.floor(Date.now() / 1000),
  });

  const data = await loadDashboardData(req.user.id);
  res.json(data);  // Returns immediately without waiting for CIO
});

Step 5: Regional Routing

// lib/customerio-region.ts
import { TrackClient, APIClient, RegionUS, RegionEU } from "customerio-node";

// Route to nearest Customer.io region based on configuration
// US accounts: track.customer.io / api.customer.io
// EU accounts: track-eu.customer.io / api-eu.customer.io

interface CioRegionalConfig {
  us: { siteId: string; trackKey: string; appKey: string };
  eu: { siteId: string; trackKey: string; appKey: string };
}

function getClientForUser(
  config: CioRegionalConfig,
  userRegion: "us" | "eu"
): { track: TrackClient; api: APIClient } {
  const creds = config[userRegion];
  const region = userRegion === "eu" ? RegionEU : RegionUS;

  return {
    track: new TrackClient(creds.siteId, creds.trackKey, { region }),
    api: new APIClient(creds.appKey, { region }),
  };
}

Performance Monitoring

// Wrap operations to measure latency
async function timedCioCall<T>(
  operation: string,
  fn: () => Promise<T>
): Promise<T> {
  const start = Date.now();
  try {
    const result = await fn();
    const elapsed = Date.now() - start;
    console.log(`CIO ${operation}: ${elapsed}ms`);
    return result;
  } catch (err) {
    const elapsed = Date.now() - start;
    console.error(`CIO ${operation} FAILED: ${elapsed}ms`);
    throw err;
  }
}

Error Handling

IssueSolution
High p99 latencyEnable connection pooling, check DNS resolution
Timeout errorsIncrease timeout, reduce payload size
Memory growthCap LRU cache size, limit batch buffer
Dedup cache missesIncrease TTL if same identify calls are >5min apart

Resources

Next Steps

After performance tuning, proceed to customerio-cost-tuning for cost optimization.

平台分布

Codex

33.28%
按下载量换算62

Claude

30.76%
按下载量换算57

Cursor

20.07%
按下载量换算37

Gemini CLI

9.66%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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