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SEO

审计、规划并实施 SEO 改进,涵盖技术 SEO、页面优化、结构化数据、核心网页指标和内容策略。当用户希望提升搜索可见性、进行 SEO 修复、schema 标记、站点地图/robots 文件处理或关键词映射时使用。

SEO 是什么?

该 SEO 技能提供了一套系统的方法来提升网站在搜索引擎中的可见性,专注于技术正确性、性能优化和内容相关性,而不是使用操纵手段。它指导用户执行技术 SEO 检查清单,涵盖爬取性、可索引性、核心网页指标和结构化数据。该技能还包括页面内优化规则,如标题标签、元描述和标题结构,以及关键词映射和内部链接的策略。通过遵循这些原则,用户可以从审计和规划到实施可执行的、针对页面的改进,最终在不依赖黑帽技术的情况下实现可持续的搜索可见性。它支持多个平台,包括 Claude Code、Cursor 和 Codex。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/seo

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

SEO 是做什么的?

通过技术正确性、性能和内容相关性提升搜索可见性,而非使用花招。

何时使用

遇到以下情况时使用此技能:

  • 审计爬取性、可索引性、canonical 标签或重定向
  • 改进标题标签、元描述和标题结构
  • 添加或验证结构化数据
  • 改进核心网页指标
  • 进行关键词研究并将关键词映射到 URL
  • 规划内部链接或站点地图 / robots.txt 更改

工作原理

原则

  1. 先修复技术障碍,再进行内容优化。
  2. 一个页面应有一个明确的主要搜索意图。
  3. 优先考虑长期质量信号,而非操纵模式。
  4. 移动优先假设很重要,因为索引是移动优先的。
  5. 建议应针对具体页面且可实施。

技术 SEO 检查清单

爬取性

  • robots.txt 应允许重要页面并阻止低价值内容
  • 不应有无意中设置 noindex 的重要页面
  • 重要页面应在浅层点击深度内可访问
  • 避免超过两个跳转的重定向链
  • canonical 标签应自洽且无循环

可索引性

  • 首选 URL 格式应保持一致
  • 多语言页面如未使用 hreflang 则需正确设置
  • 站点地图应反映预期的公开页面
  • 无 duplicate URL 在没有 canonical 控制的情况下竞争

性能

  • LCP < 2.5s
  • INP < 200ms
  • CLS < 0.1
  • 常见修复:预加载核心资源、减少渲染阻塞工作、预留布局空间、精简繁重 JS

结构化数据

  • 首页:适当使用组织或商业 schema
  • 编辑文章:Article / BlogPosting
  • 产品页:Product 和 Offer
  • 内部页面:BreadcrumbList
  • Q&A 版块:仅在内容完全匹配时使用 FAQPage

页面内规则

标题标签

  • 长度大约为 50-60 个字符
  • 将主要关键词或概念放在前端
  • 标题对人类易读,而非堆砌关键词给机器人

元描述

  • 长度大约为 120-160 个字符
  • 诚实描述页面内容
  • 自然地包含主要话题

标题结构

  • 一个清晰的 H1
  • H2 和 H3 应反映实际内容层级
  • 不要仅仅为了视觉样式而跳过结构

关键词映射

  1. 定义搜索意图
  2. 收集真实的关键词变体
  3. 根据意图匹配度、可能的价值和竞争程度进行优先级排序
  4. 将一个主要关键词/主题映射到一个 URL
  5. 检测并避免关键词自相残杀

内部链接

  • 从权重高的页面链接到你希望排名的页面
  • 使用描述性锚文本
  • 在可能的情况下避免使用通用锚文本
  • 从新页面反向填充链接到相关的现有页面

示例

标题公式

主要话题 - 具体修饰符 | 品牌

元描述公式

行动 + 话题 + 价值主张 + 一个支撑细节

JSON-LD 示例

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "此处为页面标题",
  "author": {
    "@type": "Person",
    "name": "作者姓名"
  },
  "publisher": {
    "@type": "Organization",
    "name": "品牌名称"
  }
}

审计输出格式

[高] 产品页面上的重复标题标签
位置: src/routes/products/[slug].tsx
问题: 动态标题退化为相同默认字符串,削弱相关性并产生重复信号。
修复: 使用产品名称和主要类别为每个产品生成唯一标题。

反模式

反模式修复方法
关键词堆砌首先为用户写作
内容单薄的重复页面合并或区分它们
使用不存在的内容的 schema使 schema 匹配实际情况
不检查实际页面就提供内容建议先阅读真实页面
通用的“改善 SEO”输出将每条建议与特定页面或资源关联

相关技能

  • seo-specialist
  • frontend-patterns
  • brand-voice
  • market-research

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

相关技能