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algo-seo-technicalalgo SEO technical 搜索

Agent Skill

algo-seo-technical 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。

总安装

862

周安装

17

GitHub Stars

125

下载量

140
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

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:algo-seo-technical(algo SEO technical 搜索)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/algo-seo-technical
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-seo-technical
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/asgard-ai-platform/skills --skill algo-seo-technical

简介

algo-seo-technical 聚焦核心网页指标(Core Web Vitals)诊断与优化。

  • 适用于解决 LCP、INP、CLS 不达标问题以提升用户体验和 SEO 排名的场景。
  • 基于真实用户数据(CrUX)测量,目标值为 LCP<2.5s、INP<200ms、CLS<0.1。
  • 安装需从指定仓库获取,使用前请确认是否具备前端资源修改权限。
  • 不处理内容相关性或外链质量,专责技术性性能指标改善。

SKILL.md

Core Web Vitals Optimization

Overview

Core Web Vitals are Google's page experience metrics: LCP (loading), INP (interactivity), and CLS (visual stability). Measured on real user data (CrUX). Pass thresholds: LCP < 2.5s, INP < 200ms, CLS < 0.1.

When to Use

Trigger conditions:

  • Diagnosing why a site fails Core Web Vitals assessment
  • Optimizing page load performance for SEO
  • Reducing layout shift or improving interactivity

When NOT to use:

  • When the issue is content relevance, not speed (use content SEO)
  • When analyzing link authority (use PageRank / backlink analysis)

Algorithm

IRON LAW: CrUX Field Data Is the Source of Truth
Lab scores (Lighthouse) that pass can still FAIL in the field.
Google ranks based on REAL USER data (75th percentile):
- LCP < 2.5s (Good), 2.5-4.0s (Needs Improvement), > 4.0s (Poor)
- INP < 200ms (Good), 200-500ms (Needs Improvement), > 500ms (Poor)
- CLS < 0.1 (Good), 0.1-0.25 (Needs Improvement), > 0.25 (Poor)

Phase 1: Input Validation

Collect field data from CrUX API or Search Console. Run Lighthouse for lab baseline. Identify which metrics fail. Gate: Have both field and lab data; failing metrics identified.

Phase 2: Core Algorithm

LCP fixes: 1. Optimize largest element (hero image/text). 2. Preload critical resources. 3. Reduce server response time (TTFB). 4. Eliminate render-blocking resources.

INP fixes: 1. Break long tasks (> 50ms) into smaller chunks. 2. Reduce JavaScript execution time. 3. Use requestIdleCallback for non-critical work. 4. Minimize main thread blocking.

CLS fixes: 1. Set explicit dimensions on images/videos. 2. Reserve space for ads/embeds. 3. Avoid inserting content above existing content. 4. Use CSS contain for dynamic elements.

Phase 3: Verification

Re-run Lighthouse, deploy, then monitor CrUX for 28-day rolling average improvement. Gate: Lab scores pass; await field data confirmation (28-day cycle).

Phase 4: Output

Return audit results with specific fix recommendations prioritized by impact.

Output Format

{
  "audit": {"lcp": {"value_ms": 3200, "status": "poor", "element": "hero-image.jpg", "fixes": ["preload", "compress"]}},
  "metadata": {"url": "...", "data_source": "crux", "device": "mobile"}
}

Examples

Sample I/O

Input: URL with LCP=4.1s, CLS=0.32, INP=150ms Expected: LCP and CLS flagged as poor; INP passes. Fix priorities: CLS (image dimensions) → LCP (hero image preload)

Edge Cases

InputExpectedWhy
SPA with client renderingHigh LCP likelyNo server-rendered content for LCP element
Page with adsHigh CLS likelyAd slots inject content dynamically
All metrics pass in labMay still fail fieldReal devices on slow networks differ from lab

Gotchas

  • Lab vs field gap: Lighthouse runs on a simulated fast device. Real users on 3G with old phones produce very different numbers.
  • LCP element changes: The LCP element can differ across page loads (image vs text). Optimize for the MOST COMMON LCP element, not just one.
  • CLS attribution: Layout shifts are blamed on the element that moved, but the CAUSE is often an element inserted above it. Trace the cause, not the symptom.
  • INP replaced FID: As of March 2024, INP replaces FID. Old references to FID are outdated.
  • 28-day lag: CrUX uses a 28-day rolling window. Fixes take up to a month to reflect in field data.

References

  • For element-specific optimization techniques, see references/optimization-techniques.md
  • For CrUX API usage and monitoring setup, see references/crux-monitoring.md

适合场景

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02

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03

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能力概览

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能力 2

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能力 3

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能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

33.22%
按下载量换算47

Claude

30.9%
按下载量换算43

Cursor

19.47%
按下载量换算27

Gemini CLI

8.15%
按下载量换算11

安全审计

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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

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来源信息

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