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Agent Skill · 研究检索

webperf-resources(Webperf 资源)

webperf-resources 用于查找、检索和筛选相关信息,适合在 OpenClaw 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。

webperf-resources 是研究检索类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为调用外部服务;许可证为 MIT-0,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 13,174。

总安装 9,621周安装 393GitHub Stars 公开资料未说明下载 3,081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
MIT-0
版本
0.1.0

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:webperf-resources(Webperf 资源)
来源仓库:https://github.com/nucliweb/webperf-resources
安装命令:
openclaw skills install webperf-resources
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 OpenClaw 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。

ClawHubOpenClaw
openclaw skills install webperf-resources

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。

webperf-resources 用于智能网络质量分析,检测连接类型和带宽。自适应加载策略根据 2g/3g/4g、RTT 和数据节省模式调整资源加载。优化网络资源分配,提升弱网环境下的用户体验。安装命令为 openclaw skills install webperf-resources,需确认网络权限。

建议结合来源仓库 README 核验具体用法,注意是否会触发命令执行或文件读写。

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 OpenClaw;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 openclaw skills install webperf-resources;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

    它需要调用外部服务,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

webperf-resources 怎么安装?

在终端执行 openclaw skills install webperf-resources。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

webperf-resources 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。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。

webperf-resources 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

webperf-resources 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

webperf-resources 用的人多吗?

公开数据是累计安装 9,621 次,近一周安装 393 次,下载 3,081 次。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。

webperf-resources 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

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。

先想清楚的地方

  • 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 OpenClaw,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
webperf-resources

Webperf 资源 · 本页

9,621—调用外部服务MIT-0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

name
webperf-resources
description
Intelligent network quality analysis with adaptive loading strategies. Detects connection type (2g/3g/4g), bandwidth, RTT, and save-data mode, then automatically triggers appropriate optimization workflows. Includes decision trees that recommend image compression for slow connections, critical CSS inlining for high RTT, and save-data optimizations (disable autoplay, reduce quality). Features connection-aware performance budgets (500KB for 2g, 1.5MB for 3g, 3MB for 4g+) and adaptive loading implementation guides. Cross-skill integration with Loading (TTFB impact), Media (responsive images), and Core Web Vitals (connection impact on LCP/INP). Use when the user asks about slow connections, mobile optimization, save-data support, or adaptive loading strategies. Compatible with Chrome DevTools MCP.
license
MIT
metadata
author
Joan Leon | @nucliweb
version
1.0.0
mcp-server
chrome-devtools
category
web-performance
repository
https://github.com/nucliweb/webperf-snippets

WebPerf: Resources & Network

JavaScript snippets for measuring web performance in Chrome DevTools. Execute with mcp__chrome-devtools__evaluate_script, capture output with mcp__chrome-devtools__get_console_message.

Scripts

  • scripts/Network-Bandwidth-Connection-Quality.js — Network Bandwidth & Connection Quality

Descriptions and thresholds: references/snippets.md

Common Workflows

Network Quality Assessment

When the user asks about network performance, connection quality, or adaptive loading:

  1. Network-Bandwidth-Connection-Quality.js - Analyze network bandwidth, effective connection type, RTT, downlink, save-data mode

Adaptive Loading Strategy

When implementing adaptive loading or the user asks "how to optimize for slow connections":

  1. Network-Bandwidth-Connection-Quality.js - Detect connection quality
  2. Cross-reference with webperf-loading skill:

- TTFB.js (measure server response on slow connections) - Find-render-blocking-resources.js (identify heavy resources to defer) - Resource-Hints-Validation.js (optimize preconnect for slow networks)

  1. Cross-reference with webperf-media skill:

- Image-Element-Audit.js (implement responsive images based on connection) - Video-Element-Audit.js (adjust video quality based on connection)

Save-Data Mode Detection

When the user asks about save-data or data-saving features:

  1. Network-Bandwidth-Connection-Quality.js - Check if save-data is enabled
  2. If save-data is enabled, recommend:

- Reducing image quality - Disabling autoplay videos - Deferring non-critical resources - Using low-res thumbnails

Mobile/Slow Connection Optimization

When the user asks "optimize for mobile" or "slow connection users":

  1. Network-Bandwidth-Connection-Quality.js - Assess connection type
  2. Cross-reference with webperf-loading skill:

- TTFB.js (critical for slow connections) - Find-render-blocking-resources.js (minimize blocking on slow networks) - Critical-CSS-Detection.js (inline critical CSS to reduce RTT) - Prefetch-Resource-Validation.js (avoid excessive prefetch on slow connections)

Decision Tree

Use this decision tree to automatically run follow-up snippets based on results:

After Network-Bandwidth-Connection-Quality.js

  • If effectiveType is "slow-2g" or "2g" → Very slow connection, recommend:

1. Run webperf-loading:Critical-CSS-Detection.js (inline critical CSS) 2. Run webperf-media:Image-Element-Audit.js (implement aggressive lazy loading) 3. Run webperf-loading:Prefetch-Resource-Validation.js (remove prefetch to save bandwidth) 4. Recommend minimal resource strategy

  • If effectiveType is "3g" → Moderate connection, recommend:

1. Run webperf-loading:Find-render-blocking-resources.js (minimize blocking) 2. Run webperf-media:Image-Element-Audit.js (responsive images) 3. Run webperf-loading:Resource-Hints-Validation.js (optimize preconnect) 4. Implement adaptive image quality

  • If effectiveType is "4g" or better → Good connection, recommend:

1. Standard optimization practices 2. Consider strategic prefetch for navigation 3. Higher quality media is acceptable

  • If save-data is enabled → User explicitly wants to save data, recommend:

1. Reduce image quality aggressively 2. Disable autoplay videos 3. Defer non-critical resources 4. Remove prefetch/preload hints 5. Show "high quality" toggle option

  • If RTT > 300ms → High latency, recommend:

1. Run webperf-loading:TTFB.js (latency impacts TTFB) 2. Run webperf-loading:Resource-Hints-Validation.js (preconnect critical for high RTT) 3. Minimize number of origins 4. Use HTTP/2 or HTTP/3 for multiplexing

  • If downlink < 1 Mbps → Very limited bandwidth, recommend:

1. Run webperf-media:Image-Element-Audit.js (aggressive compression) 2. Run webperf-media:Video-Element-Audit.js (disable autoplay) 3. Run webperf-loading:Prefetch-Resource-Validation.js (remove prefetch) 4. Implement bandwidth-aware loading

  • If downlink > 10 Mbps → Good bandwidth, consider:

- Higher quality media - Strategic prefetch - Preloading next-page resources

Cross-Skill Triggers

These triggers recommend using snippets from other skills:

From Resources to Loading Skill
  • If slow connection detected (2g/3g) → Use webperf-loading skill:

- TTFB.js (latency is amplified on slow connections) - Critical-CSS-Detection.js (reduce RTT by inlining critical CSS) - Find-render-blocking-resources.js (minimize blocking resources) - Resource-Hints-Validation.js (preconnect is critical for high RTT) - Prefetch-Resource-Validation.js (avoid prefetch on slow connections)

  • If high RTT detected (>200ms) → Use webperf-loading skill:

- TTFB-Sub-Parts.js (break down latency components) - Resource-Hints-Validation.js (preconnect to reduce RTT impact) - Service-Worker-Analysis.js (caching is critical for high latency)

From Resources to Media Skill
  • If slow connection or save-data detected → Use webperf-media skill:

- Image-Element-Audit.js (implement responsive images, aggressive compression) - Video-Element-Audit.js (disable autoplay, reduce quality)

From Resources to Core Web Vitals Skill
  • If slow connection detected → Check Core Web Vitals impact:

- Use webperf-core-web-vitals:LCP.js (LCP is heavily impacted by slow connections) - Use webperf-core-web-vitals:INP.js (high latency can impact interaction responsiveness)

Adaptive Loading Implementation Guide

Based on Network Information API results, implement these strategies:

For slow-2g / 2g (< 50 Kbps):

// Detected by effectiveType: "slow-2g" or "2g"
Strategies:
- Serve low-res images (quality: 30-40)
- Disable autoplay videos
- Remove all prefetch hints
- Inline all critical CSS
- Defer all non-critical JavaScript
- Use system fonts (no webfonts)
- Aggressive lazy loading (load on scroll + buffer)

For 3g (50-700 Kbps):

// Detected by effectiveType: "3g"
Strategies:
- Serve medium-res images (quality: 60-70)
- Disable autoplay videos
- Limited prefetch (critical only)
- Inline critical CSS only
- Defer non-critical JavaScript
- Preload 1-2 critical fonts
- Standard lazy loading

For 4g+ (> 700 Kbps):

// Detected by effectiveType: "4g"
Strategies:
- Serve high-res images (quality: 80-90)
- Allow autoplay videos (muted)
- Strategic prefetch for navigation
- Standard CSS loading
- Standard JavaScript loading
- Preload critical fonts
- Standard lazy loading

For save-data mode:

// Detected by navigator.connection.saveData === true
Strategies:
- Override connection type, treat as worse than actual
- Show "Load high quality" toggle
- Disable autoplay entirely
- Minimal images, minimal quality
- No prefetch, no preload (except critical)

Performance Budget by Connection Type

Adjust performance budgets based on connection quality:

slow-2g / 2g:

  • Total page weight: < 500KB
  • Images: < 200KB total
  • JavaScript: < 100KB total
  • No videos

3g:

  • Total page weight: < 1.5MB
  • Images: < 800KB total
  • JavaScript: < 300KB total
  • Videos: < 3MB (only if critical)

4g+:

  • Total page weight: < 3MB
  • Images: < 2MB total
  • JavaScript: < 1MB total
  • Videos: < 10MB

Real-World Scenarios

Scenario: User on 3G reports slow page load

  1. Run Network-Bandwidth-Connection-Quality.js → confirms 3g
  2. Run webperf-loading:TTFB.js → high TTFB due to latency
  3. Run webperf-loading:Critical-CSS-Detection.js → CSS not inlined
  4. Recommendation: Inline critical CSS, implement adaptive loading

Scenario: User with save-data enabled complains about data usage

  1. Run Network-Bandwidth-Connection-Quality.js → saveData: true
  2. Run webperf-media:Image-Element-Audit.js → high-res images served
  3. Run webperf-media:Video-Element-Audit.js → autoplay enabled
  4. Recommendation: Respect save-data, reduce quality, disable autoplay

Scenario: International users report slow LCP

  1. Run Network-Bandwidth-Connection-Quality.js → high RTT (300ms+)
  2. Run webperf-core-web-vitals:LCP.js → LCP 4s+
  3. Run webperf-loading:TTFB-Sub-Parts.js → DNS + connection = 500ms
  4. Recommendation: Use CDN, implement preconnect, optimize for latency

Scenario: Mobile users in rural areas

  1. Run Network-Bandwidth-Connection-Quality.js → 2g, high RTT, low downlink
  2. Run webperf-loading:Find-render-blocking-resources.js → many blocking
  3. Run webperf-media:Image-Element-Audit.js → all images eager-loaded
  4. Recommendation: Aggressive adaptive loading, inline critical CSS, lazy load all images

Network Information API Limitations

Be aware of API limitations and fallbacks:

API not available:

  • Safari does not support Network Information API
  • Fallback: Use TTFB as proxy for connection quality
  • Fallback: Device detection (mobile = assume slow)
  • Fallback: User preference toggle

API values are estimates:

  • effectiveType is based on recent observations
  • Values can change during session
  • Re-check periodically for long sessions

Privacy considerations:

  • Some browsers limit precision for privacy
  • Values may be rounded or capped
  • Consider user privacy when making decisions

Testing Adaptive Loading

To test adaptive loading implementations:

  1. Use Chrome DevTools Network Throttling
  2. Run Network-Bandwidth-Connection-Quality.js at each throttling level
  3. Verify adaptive strategies activate correctly
  4. Measure Core Web Vitals at each connection speed
  5. Adjust breakpoints and strategies based on results

Test matrix:

  • Offline
  • slow-2g (50 Kbps, RTT 2000ms)
  • 2g (250 Kbps, RTT 300ms)
  • 3g (750 Kbps, RTT 100ms)
  • 4g (4 Mbps, RTT 20ms)
  • save-data enabled at each level

References

  • references/snippets.md — Descriptions and thresholds for each script
  • references/schema.md — Return value schema for interpreting script output
Execute via mcp__chrome-devtools__evaluate_script → read with mcp__chrome-devtools__get_console_message.

平台分布

OpenClaw

76.42%
按下载量换算2,355

安全审计

VirusTotal

通过

ClawScan

可疑

Static analysis

通过

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

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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