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Agent Skill · 研究检索

instantly-performance-tuning(即时性能调整)

instantly-performance-tuning 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

instantly-performance-tuning 是研究检索类 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,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 54,403。

总安装 512周安装 22GitHub Stars 2,107下载 180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:instantly-performance-tuning(即时性能调整)
来源仓库:https://github.com/jeremylongshore/claude-code-plugins-plus-skills
仓库路径:skills/instantly-performance-tuning
安装命令:
npx skills add https://github.com/jeremylongshore/claude-code-plugins-plus-skills --skill instantly-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 instantly-performance-tuning

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

instantly-performance-tuning 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。适用于关键词搜索、任务场景匹配或来源线索梳理等研究检索类工作。通过 npx skills add 命令从指定 GitHub 仓库安装并使用。

安装前需确认权限范围和维护状态,注意是否涉及联网、命令执行或文件读写操作。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/instantly-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

instantly-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。

instantly-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。

instantly-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。

instantly-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。

instantly-performance-tuning 用的人多吗?

公开数据是累计安装 512 次,近一周安装 22 次,下载 180 次,GitHub 2,107 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。

instantly-performance-tuning 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

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。
  • 来源仓库有 2,107 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。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 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。
  • 需要处理研究检索类任务、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。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
instantly-performance-tuning

即时性能调整 · 本页

5122,107需要联网未明确有提醒

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

Instantly Performance Tuning

Overview

Optimize Instantly API v2 integrations for speed and throughput. Key areas: caching analytics data, batching lead operations, concurrent request management, efficient pagination, and connection reuse. The email listing endpoint has a strict 20 req/min limit that requires special handling.

Prerequisites

  • Completed instantly-install-auth setup
  • Working Instantly integration
  • Understanding of async patterns and caching strategies

Instructions

Step 1: Cache Analytics Data

Campaign analytics don't change every second — cache them for 5-15 minutes to avoid redundant API calls.

class InstantlyCache {
  private cache = new Map<string, { data: unknown; expiry: number }>();

  get<T>(key: string): T | null {
    const entry = this.cache.get(key);
    if (!entry || Date.now() > entry.expiry) {
      this.cache.delete(key);
      return null;
    }
    return entry.data as T;
  }

  set(key: string, data: unknown, ttlMs: number) {
    this.cache.set(key, { data, expiry: Date.now() + ttlMs });
  }
}

const cache = new InstantlyCache();

async function getCachedAnalytics(campaignId: string) {
  const cacheKey = `analytics:${campaignId}`;
  const cached = cache.get<CampaignAnalytics>(cacheKey);
  if (cached) return cached;

  const data = await instantly<CampaignAnalytics>(
    `/campaigns/analytics?id=${campaignId}`
  );
  cache.set(cacheKey, data, 5 * 60 * 1000); // 5 min TTL
  return data;
}

// Cache campaign list (changes infrequently)
async function getCachedCampaigns() {
  const cacheKey = "campaigns:all";
  const cached = cache.get<Campaign[]>(cacheKey);
  if (cached) return cached;

  const campaigns = await instantly<Campaign[]>("/campaigns?limit=100");
  cache.set(cacheKey, campaigns, 15 * 60 * 1000); // 15 min TTL
  return campaigns;
}

Step 2: Batch Lead Operations with Controlled Concurrency

interface BatchResult<T> {
  succeeded: T[];
  failed: Array<{ input: unknown; error: string }>;
  duration: number;
}

async function batchAddLeads(
  campaignId: string,
  leads: Array<{ email: string; first_name?: string; company_name?: string }>,
  options = { concurrency: 5, delayMs: 200, retries: 3 }
): Promise<BatchResult<Lead>> {
  const start = Date.now();
  const succeeded: Lead[] = [];
  const failed: Array<{ input: unknown; error: string }> = [];
  let active = 0;

  const addWithRetry = async (lead: typeof leads[0]) => {
    for (let attempt = 0; attempt <= options.retries; attempt++) {
      try {
        const result = await instantly<Lead>("/leads", {
          method: "POST",
          body: JSON.stringify({
            campaign: campaignId,
            email: lead.email,
            first_name: lead.first_name,
            company_name: lead.company_name,
            skip_if_in_workspace: true,
          }),
        });
        succeeded.push(result);
        return;
      } catch (err: any) {
        if (err.status === 429) {
          await new Promise((r) => setTimeout(r, Math.pow(2, attempt) * 1000));
          continue;
        }
        if (attempt === options.retries) {
          failed.push({ input: lead, error: err.message });
        }
      }
    }
  };

  // Process in chunks
  for (let i = 0; i < leads.length; i += options.concurrency) {
    const chunk = leads.slice(i, i + options.concurrency);
    await Promise.allSettled(chunk.map(addWithRetry));

    if (i + options.concurrency < leads.length) {
      await new Promise((r) => setTimeout(r, options.delayMs));
    }

    // Progress report
    const progress = Math.min(i + options.concurrency, leads.length);
    console.log(`Progress: ${progress}/${leads.length} (${succeeded.length} ok, ${failed.length} failed)`);
  }

  return { succeeded, failed, duration: Date.now() - start };
}

Step 3: Efficient Pagination

// Pre-fetch next page while processing current page
async function* prefetchPaginate<T extends { id: string }>(
  path: string,
  pageSize = 100
): AsyncGenerator<T[]> {
  let startingAfter: string | undefined;
  let nextPagePromise: Promise<T[]> | null = null;

  const fetchPage = (after?: string) => {
    const qs = new URLSearchParams({ limit: String(pageSize) });
    if (after) qs.set("starting_after", after);
    return instantly<T[]>(`${path}?${qs}`);
  };

  // Fetch first page
  let currentPage = await fetchPage();

  while (currentPage.length > 0) {
    // Start fetching next page immediately
    if (currentPage.length === pageSize) {
      const lastId = currentPage[currentPage.length - 1].id;
      nextPagePromise = fetchPage(lastId);
    } else {
      nextPagePromise = null;
    }

    yield currentPage;

    if (!nextPagePromise) break;
    currentPage = await nextPagePromise;
  }
}

// Usage — processes next page while current page is being handled
for await (const batch of prefetchPaginate<Lead>("/leads/list")) {
  for (const lead of batch) {
    // Process lead — next page is already loading
  }
}

Step 4: Connection Reuse with Keep-Alive

import { Agent } from "undici";

// Create a persistent connection pool
const dispatcher = new Agent({
  keepAliveTimeout: 30000,     // keep connections alive for 30s
  keepAliveMaxTimeout: 60000,
  connections: 10,             // max 10 concurrent connections
  pipelining: 1,
});

async function instantlyPooled<T>(path: string, options: RequestInit = {}): Promise<T> {
  const url = `https://api.instantly.ai/api/v2${path}`;
  const res = await fetch(url, {
    ...options,
    headers: {
      "Content-Type": "application/json",
      Authorization: `Bearer ${process.env.INSTANTLY_API_KEY}`,
      ...options.headers,
    },
    // @ts-ignore — undici dispatcher
    dispatcher,
  });

  if (!res.ok) throw new Error(`Instantly ${res.status}: ${await res.text()}`);
  return res.json() as Promise<T>;
}

Step 5: Throttled Email Fetcher (20 req/min limit)

class ThrottledEmailClient {
  private timestamps: number[] = [];
  private readonly maxPerMinute = 18; // leave margin

  private async throttle() {
    const now = Date.now();
    this.timestamps = this.timestamps.filter((t) => now - t < 60000);

    if (this.timestamps.length >= this.maxPerMinute) {
      const wait = 60000 - (now - this.timestamps[0]) + 500;
      await new Promise((r) => setTimeout(r, wait));
    }
    this.timestamps.push(Date.now());
  }

  async listEmails(params: { campaign_id?: string; limit?: number; starting_after?: string }) {
    await this.throttle();
    const qs = new URLSearchParams();
    if (params.campaign_id) qs.set("campaign_id", params.campaign_id);
    if (params.limit) qs.set("limit", String(params.limit));
    if (params.starting_after) qs.set("starting_after", params.starting_after);
    return instantly(`/emails?${qs}`);
  }

  async getUnreadCount() {
    await this.throttle();
    return instantly("/emails/unread/count");
  }
}

Performance Benchmarks

OperationUnoptimizedOptimizedImprovement
500 lead import~250s (sequential)~30s (5 concurrent + batch)8x
Campaign analytics (10 queries)10 API calls1 API call (cached)10x
All campaigns page load~2s (no cache)~50ms (cached)40x
Lead pagination (10K leads)~100s (sequential)~50s (prefetch)2x

Error Handling

ErrorCauseSolution
429 during batch importToo many concurrent requestsReduce concurrency, increase delay
429 on email listing>20 req/minUse ThrottledEmailClient
Stale cache dataTTL too longReduce TTL or add cache invalidation
Memory issuesLarge pagination result setUse async generators, process in chunks

Resources

Next Steps

For cost optimization, see instantly-cost-tuning.

平台分布

Codex

37.86%
按下载量换算68

Claude

31.84%
按下载量换算57

Cursor

17.64%
按下载量换算32

Gemini CLI

8.8%
按下载量换算16

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

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。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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