SkillAtlasSkill 详情

apify-performance-tuning

A model-agnostic agent-skills platform.

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项目 README

来源文件：README.md

抓取于 2026年8月27日

Tons of Skills

A model-agnostic agent-skills platform. The canonical layer is harness-free by construction; Claude Code is currently the verified-native harness. Other harnesses remain engineering candidates until their native-path integration is verified; source research alone is never presented as public support.

Release CLI Plugins Skills GitHub Stars skills.sh Sponsor: Kobiton Buy me a monster

ko-fi

Version semantics: the release badge is this marketplace's display version. npm packages, including the ccpi CLI and publishable plugins, retain their own package versions; they are intentionally not expected to equal the display version. The version-surface checker governs the display surfaces without rewriting package semver.

Install

Inside Claude Code, one command installs the whole marketplace:

/plugin marketplace add jeremylongshore/claude-code-plugins

Or use the CLI:

pnpm add -g @intentsolutionsio/ccpi
ccpi install devops-automation-pack

Browse the marketplace · Explore plugins · Download bundles

Killer Skill of the Week — no-ai-slop by Peter Yang

Strip AI slop from any draft — named-pattern edits that keep the writer's real voice

no-ai-slop does two jobs and refuses to fake a third. In Edit mode it makes the minimum effective edit — cutting throat-clearing, weak verbs, and abstract nouns while deliberately preserving the writer's cadence, bluntness, humor, and honest admissions, so a rough draft still sounds like the same person afterward. In Detect mode it names each AI-slop pattern it finds, quotes the offending line, and gives the fix in a few words — and pointedly does NOT score the draft or guess whether an AI wrote it. That restraint is the whole point: AI detectors guess; named patterns are evidence the reader can check. MIT-licensed, single focused skill, actively maintained by Peter Yang.

"AI detectors guess. Named patterns are evidence the user can check." — Peter Yang

Grade: A | Week of July 22, 2026 (W30) | View on GitHub

Previous picks: tonone, mnemos, databricks-pack, kobiton-automate, skyvern, code-cleanup, web-analytics, token-optimizer, executive-assistant-skills, skill-creator, cursor-pack, crypto-portfolio-tracker. See all at tonsofskills.com.

Scale, labeled

Every number below names the cohort it counts and the command that reproduces it — an unlabeled count is how a corpus ends up with five contradictory answers to "how many skills."

CountCohortReproduce with
442catalog plugins (catalog-entry cohort)node scripts/generate-readme-toc.mjs over marketplace.extended.json
3,067marketplace-visible skills (distinct)node -e "import('./scripts/corpus-resolver.mjs').then(m=>console.log(m.resolveCorpus('marketplace-visible').length))"
347agent definitions in pluginsgit ls-files 'plugins/**' | grep '/agents/.*\.md'
19plugin categoriesls -d plugins/*/

📦 Live npm Downloads

Across 396 published packages in the claude-code-plugins namespace. Updated daily by GitHub Actions.

WindowAll packagesEstablished (>30d)
Last 24 hours962962
Last 7 days2,9202,916
Last 30 days12,86812,779

"Established" excludes packages first published within the last 30 days, so a bulk-publish event doesn't dominate the headline.

Top 10 by last 30 days:

#PackageLast 30d
1@intentsolutionsio/openrouter-pack556
2@intentsolutionsio/groq-pack496
3@intentsolutionsio/databricks-pack274
4@intentsolutionsio/clickhouse-pack273
5@intentsolutionsio/wallet-security-auditor263
6@intentsolutionsio/notion-pack258
7@intentsolutionsio/elevenlabs-pack244
8@intentsolutionsio/freshie-inventory-manager214
9@intentsolutionsio/supabase-pack210
10@intentsolutionsio/agency-os204

Last refreshed 2026-08-19T03:03:05.709Z.

Ways in

Five real questions, five doors — each resolves to a live, generated surface, never a hand-maintained list:

Browse by category

The 19 categories below link into the live marketplace. Plugin counts are the catalog-entry cohort — regenerated from marketplace.extended.json by this generator; the catalog itself lives on tonsofskills.com, never in this file (§ 6A of the platform blueprint).

CategoryPlugins
🤖AI & Machine Learning36
🎭AI Agents & Agency10
🔌API Development26
💼Business Tools6
👥Community21
₿Crypto & Web327
💾Database26
🎨Design2
🔧DevOps & Infrastructure36
📚Examples & Templates5
🧩MCP Servers16
📦Packages5
⚡Performance25
✅Productivity30
🎁SaaS Skill Packs106
🔐Security27
✨Skill Enhancers9
🧪Testing28
📁Analytics1

What the classes mean

Four artifact classes live in this repository, distinguished on sight and never blurred — provenance is a truth requirement here, not a UX nicety:

ClassWhat it isHow the reader can tell
Canonical skillFirst-party, harness-free, the source of truthNo .source.json in its plugin directory
Generated adapterA thin, machine-produced harness projectionLives under a generated path with a "generated — do not edit" header
First-party packageAn Intent Solutions distribution (npm, cowork zip)@intentsolutionsio scope, IS-authored license
Upstream mirrorSomebody else's work, hosted mirror-by-default.source.json present — upstream author, license, and pinned commit recorded

Certification

Not yet certified. The certification program (tiers T0–T4 with retained, hash-matched evidence) is a later epic of the platform blueprint; until its report exists, no artifact on this surface claims a tier. This line is rendered from the absence of certification-report.json — honestly, not cosmetically.

Contribute

Start with the contribution guide, then the intake and review standards every submission passes through:

Governance

Provenance

External plugins are hosted mirror-by-default: the contributor's repository stays the source of truth, every mirrored source is pinned in a content lockfile, and upstream credit — author, license, resolved commit — is recorded in the mirror itself. Improvements flow by upstreaming to the author's repository, never by silently editing the mirror. The full decision record is the external-sync model.

License

MIT for the repository scaffolding and first-party tooling; each plugin carries its own license in its manifest, and mirrored plugins keep their upstream license verbatim.

其他

中风险

  • 来源需自行核对维护者身份。
  • 未检测到明显脚本安装指令。
  • 未检测到明显外部权限要求。
  • 未检测到高风险命令。
  • 扫描发现：1 条。

Codex — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/apify-performance-tuning" 文件夹复制到 Codex 的 skills 目录中。
  4. 重启 Codex 让新的 skill 生效。

Codex — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Codex 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Codex 让新的 skill 生效。

Claude Code — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/apify-performance-tuning" 文件夹复制到 Claude Code 的 skills 目录中。
  4. 重启 Claude Code 让新的 skill 生效。

Claude Code — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Claude Code 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Claude Code 让新的 skill 生效。

Cursor — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/apify-performance-tuning" 文件夹复制到 Cursor 的 skills 目录中。
  4. 重启 Cursor 让新的 skill 生效。

Cursor — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Cursor 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Cursor 让新的 skill 生效。

GitHub Copilot — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/apify-performance-tuning" 文件夹复制到 GitHub Copilot 的 skills 目录中。
  4. 重启 GitHub Copilot 让新的 skill 生效。

GitHub Copilot — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 GitHub Copilot 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 GitHub Copilot 让新的 skill 生效。

Windsurf — Git Clone 安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 克隆仓库：git clone https://github.com/jeremylongshore/tons-of-skills-marketplace.git
  3. 将 "skills/.curated/apify-performance-tuning" 文件夹复制到 Windsurf 的 skills 目录中。
  4. 重启 Windsurf 让新的 skill 生效。

Windsurf — 手动复制安装

  1. 安装前请先查看来源仓库和风险报告。
  2. 从源仓库下载 SKILL.md 及相关文件。
  3. 在 Windsurf 的 skills 目录中创建新文件夹。
  4. 将所有 skill 文件复制到新文件夹中。
  5. 重启 Windsurf 让新的 skill 生效。
查看 SKILL.md 原文
name: apify-performance-tuning
description: 'Optimize Apify Actor performance: crawl speed, memory usage, concurrency,
  and proxy rotation.

  Use when Actors are slow, consuming too much memory, or being blocked by target
  sites, or when a crawl is too expensive per run.

  Trigger with "apify performance", "optimize apify actor", "apify slow", "crawlee
  concurrency", "apify memory tuning", "scraper performance".

  '
allowed-tools: Read, Write, Edit
version: 1.5.0
license: MIT
author: Jeremy Longshore <jeremy@intentsolutions.io>
tags:
- saas
- scraping
- automation
- apify
compatibility: Designed for Claude Code

Apify Performance Tuning

Overview

Optimize Apify Actors for speed, cost, and reliability. Covers Crawlee concurrency settings, memory profiling, proxy rotation strategies, request batching, and crawler selection for different workloads.

The workflow is a repeatable loop: measure a baseline, apply one lever, re-measure. The single highest-impact lever is usually crawler choice — swapping a browser crawler for CheerioCrawler on non-JS pages is a 5-10x speedup on its own. The full six-step walkthrough, with every code block, lives in references/implementation.md.

Prerequisites

  • Existing Actor with measurable baseline performance
  • Understanding of apify-sdk-patterns
  • Access to Actor run stats in Apify Console
  • APIFY_TOKEN in the environment for reading run stats via ApifyClient

Instructions

Work the levers in order. Each step is expanded — with copy-paste code — in the reference file linked below.

  1. Measure a baseline. Pull runTimeSecs, requestsFinished, memAvgBytes, and usageTotalUsd from the run stats before changing anything. You cannot judge an optimization without a before number.
  2. Choose the right crawler. HttpCrawler/CheerioCrawler for static HTML or JSON (low memory, fast); PlaywrightCrawler/PuppeteerCrawler only when the page genuinely needs JavaScript rendering.
  3. Tune concurrency. Raise maxConcurrency for Cheerio (up to ~50); keep it low (~3-5) for browser crawlers because each page costs ~200MB. Let autoscaledPoolOptions adjust within the band.
  4. Optimize memory. Push data immediately instead of accumulating arrays; for browser crawlers, block images/CSS/fonts in preNavigationHooks and cap concurrent browsers.
  5. Right-size the memory allocation. Compute units bill on memory x duration — start low (512 MB for Cheerio) and only raise it if the Actor is memory-starved.
  6. Rotate proxies and tune requests. Start on datacenter proxies, fall back to residential on 403/blocked; use a session pool for IP rotation and ban detection.

Full step-by-step walkthrough with all code: references/implementation.md.

The minimal starting skeleton — swap a browser crawler for Cheerio and push immediately:

import { CheerioCrawler } from 'crawlee';
import { Actor } from 'apify';

const crawler = new CheerioCrawler({
  maxConcurrency: 50,             // Cheerio is cheap — parallelize hard
  maxRequestsPerMinute: 300,      // But cap the rate to protect the target
  requestHandler: async ({ $, request }) => {
    await Actor.pushData({ url: request.url, title: $('title').text().trim() });
  },
});

Output

Applying this skill produces:

  • A baseline vs. tuned metrics comparison (pages/min, avg/max memory, compute units, cost/run) drawn from the run stats.
  • A crawler and concurrency recommendation matched to whether the target pages need JS rendering.
  • A memory allocation value sized to the workload, with the compute-unit cost tradeoff made explicit.
  • A proxy and session strategy (datacenter-first with residential fallback) for reliability under anti-bot blocking.

Instrument the running crawl to confirm the gains in real time — see references/monitoring.md for the throughput logger and a before/after impact table.

Error Handling

IssueCauseSolution
Out of memory crashToo many concurrent browsersReduce maxConcurrency
Slow crawl speedLow concurrencyIncrease maxConcurrency
High failure rateAnti-bot blockingAdd proxy, reduce concurrency
Expensive runsOver-provisioned memoryProfile and reduce allocation
Stalled crawlRequest handler timeoutSet requestHandlerTimeoutSecs

Examples

Slow Playwright crawl on static pages. The Actor renders every page in a browser at 3 pages/min. The pages are server-rendered HTML, so switch to CheerioCrawler and raise maxConcurrency — ~30 pages/min (10x). Code: references/implementation.md Steps 1-2.

Out-of-memory crashes under load. A PlaywrightCrawler at maxConcurrency: 20 OOMs. Drop concurrency to 3, block images/CSS/fonts in preNavigationHooks, and call window.stop() post-navigation. Code: references/implementation.md Step 3.

Getting blocked (403s) mid-crawl. Start on datacenter proxies, and on a 403 re-enqueue the request with a residential proxy and retire the session to force a new IP. Code: references/implementation.md Step 5.

Runs cost too much. A 4GB allocation bills 8x more than needed for HTML parsing. Right-size to 512 MB and re-measure cost/run. Code: references/implementation.md Step 4; impact table in references/monitoring.md.

Resources

Next Steps

For cost optimization beyond performance tuning, see the apify-cost-tuning skill in this pack — it covers compute-unit budgeting, storage costs, and scheduling strategies that this skill's memory right-sizing feeds into.

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