Communitygithub.com

SEO

审计、规划和实施技术 SEO、页面优化、结构化数据、核心网页指标和内容策略方面的改进。当用户希望提升搜索可见性、进行 SEO 修复、使用 Schema 标记、处理站点地图/robots 文件或进行关键词映射时使用。

SEO 是什么?

SEO 技能旨在通过技术正确性、性能和内容相关性来提升搜索可见性,而非采用取巧手段。它涵盖从技术 SEO 清单(可爬性、可索引性、核心网页指标、结构化数据)到页面规则(标题标签、元描述、标题结构)以及关键词映射和内部链接策略的完整工作流程。该技能适用于任何基于文本的 AI 编码环境(如 Claude Code 或 Cursor),并遵循一套原则:先修复技术阻碍,确保每页有明确的主要搜索意图,优先采用长期质量信号,以移动优先的假设和分析驱动。它提供可直接执行的审计输出(包括代码位置和建议修复),并警告避免关键词堆砌、重复页面和虚假 Schema 等反模式。

兼容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/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,而非采用取巧手段。

使用时机

在以下情况下使用此技能:

  • 审计可爬性、可索引性、规范标签或重定向
  • 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 更改

工作原理

原则

  1. 先修复技术阻碍,再进行内容优化。
  2. 每页应有一个明确的主要搜索意图。
  3. 优先采用长期质量信号,而非操纵性模式。
  4. 移动优先假设很重要,因为索引是基于移动端的。
  5. 建议应具有页面特异性并可执行。

技术 SEO 清单

可爬性

  • robots.txt 应允许重要页面并阻止低价值页面
  • 不应有重要页面被意外设为 noindex
  • 重要页面应在较浅的点击深度内可访问
  • 避免超过两跳的重定向链
  • 规范标签应自洽且无循环

可索引性

  • 首选 URL 格式应一致
  • 多语言页面在适用情况下需使用正确的 hreflang
  • 站点地图应反映预期的公开页面
  • 不应有重复 URL 未通过规范控制相互竞争

性能

  • LCP < 2.5 秒
  • INP < 200 毫秒
  • CLS < 0.1
  • 常见修复:预加载首屏资源、减少阻塞渲染的工作、预留布局空间、精简繁重的 JS

结构化数据

  • 首页:在适当情况下使用组织或企业 Schema
  • 文章页面:Article / BlogPosting
  • 产品页面:Product 和 Offer
  • 内部页面:BreadcrumbList
  • 问答部分:仅当内容真正匹配时才使用 FAQPage

页面规则

标题标签

  • 目标长度约 50-60 个字符
  • 将主要关键词或概念放在靠前位置
  • 使标题对人类可读,而非为爬虫堆砌关键词

元描述

  • 目标长度约 120-160 个字符
  • 如实描述页面
  • 自然地包含主要主题

标题结构

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

关键词映射

  1. 定义搜索意图
  2. 收集实际的关键词变体
  3. 按意图匹配度、潜在价值和竞争程度排序
  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

  • 从强势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
  • 从新页面补充链接到相关现有页面

示例

标题公式

主要话题 - 具体修饰词 | 品牌

元描述公式

动作 + 话题 + 价值主张 + 一个支持性细节

JSON-LD 示例

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

审计输出格式

[高优] 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,削弱相关性并造成重复信号。
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
关键词堆砌优先为用户撰写内容
空洞的近似重复页面合并或区分它们
为并非实际存在的内容添加 Schema使 Schema 符合实际情况
未查看实际页面就提供内容建议先阅读实际页面
泛泛的“改进 SEO”输出将每条建议关联到页面或资产

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

相关技能