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SEO

稽核、規劃並執行 SEO 改善,涵蓋技術 SEO、頁面優化、結構化資料、網頁核心指標與內容策略。當使用者希望提升搜尋能見度、進行 SEO 修正、建立結構化資料、處理 Sitemap/robots 或關鍵字對應時使用。

SEO 是什麼?

此技能提供一套全面的 SEO 工作流程,協助 AI 代理診斷網站的可爬性、可索引性、效能與結構化資料。它遵循「先解決技術障礙,再進行內容優化」的原則,確保每項建議都具體可行且以頁面為單位。技能支援所有主要平台,並涵蓋關鍵字對應、內部連結規劃以及避免關鍵字填塞、內容重複等常見反模式。透過結構化的檢查清單 (如 LCP < 2.5s、INP < 200ms) 與 JSON-LD 範例,開發者能獲得明確的修正方向,從而提升搜尋排名與使用者體驗。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/seo

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

SEO 是做什麼的?

透過技術正確性、效能與內容相關性來提升搜尋能見度,不作弊、不取巧。

使用時機

遇到以下情況時使用此技能:

  • 稽核可爬性、可索引性、標準網址或重新導向
  • 改善標題標籤、中繼描述與標題結構
  • 新增或驗證結構化資料
  • 改善網頁核心指標
  • 進行關鍵字研究,並將關鍵字對應到 URL
  • 規劃內部連結或 Sitemap / robots 變更

運作方式

原則

  1. 先解決技術障礙,再進行內容優化。
  2. 一個頁面應有一個明確的主要搜尋意圖。
  3. 偏好長期品質訊號,而非操縱性模式。
  4. 行動版優先假設很重要,因為索引是行動版優先。
  5. 建議應針對特定頁面且可執行。

技術 SEO 檢查清單

可爬性

  • robots.txt 應允許重要頁面,並封鎖低價值內容
  • 不應有任何重要頁面被意外設定為 noindex
  • 重要頁面應可在淺層點擊深度內觸及
  • 避免超過兩次跳轉的重新導向鏈
  • 標準標籤應保持一致且不形成迴圈

可索引性

  • 偏好的 URL 格式應保持一致
  • 多語言頁面在需要時應使用正確的 hreflang
  • Sitemap 應反映預期的公開頁面範圍
  • 不應有重複的 URL 在沒有標準標籤控制下相互競爭

效能

  • LCP < 2.5 秒
  • INP < 200 毫秒
  • CLS < 0.1
  • 常見修正:預載主要資產、減少阻斷渲染的工作、保留版面空間、精簡過重的 JS

結構化資料

  • 首頁:適當時使用組織或商家結構化資料
  • 編輯頁面:Article / BlogPosting
  • 產品頁面:Product 和 Offer
  • 內部頁面:BreadcrumbList
  • Q&A 區塊:僅在內容真正符合時使用 FAQPage

頁面規則

標題標籤

  • 目標長度約 50-60 個字元
  • 將主要關鍵字或概念放在靠近開頭的位置
  • 標題應對人類可讀,而非對爬蟲塞滿關鍵字

中繼描述

  • 目標長度約 120-160 個字元
  • 誠實描述頁面內容
  • 自然納入主要主題

標題結構

  • 一個明確的 H1
  • H2 和 H3 應反映實際的內容階層
  • 不要只為了視覺樣式而跳過結構層級

關鍵字對應

  1. 定義搜尋意圖
  2. 收集實際的關鍵字變體
  3. 根據意圖匹配度、潛在價值與競爭程度排序
  4. 將一個主要關鍵字/主題對應到一個 URL
  5. 偵測並避免關鍵字自相殘殺

內部連結

  • 從權威頁面連結到你想排名的頁面
  • 使用描述性的錨點文字
  • 避免在能使用更具體錨點時使用通用錨點
  • 從新頁面補充連結到相關的現有頁面

範例

標題公式

主要主題 - 特定修飾詞 | 品牌名稱

中繼描述公式

行動 + 主題 + 價值主張 + 一個輔助細節

JSON-LD 範例

{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "這裡放頁面標題",
  "author": {
    "@type": "Person",
    "name": "作者名稱"
  },
  "publisher": {
    "@type": "Organization",
    "name": "品牌名稱"
  }
}

稽核輸出格式

[HIGH] 產品頁面出現重複的標題標籤
位置:src/routes/products/[slug].tsx
問題:動態標題都變成相同的預設字串,削弱了相關性並產生重複訊號。
修正:使用產品名稱與主要類別,為每個產品產生獨一無二的標題。

反模式

反模式修正方式
關鍵字填塞以使用者為優先撰寫內容
內容過於接近的重複頁面合併或區隔它們
針對不存在的內容使用結構化資料讓結構化資料符合實際內容
未實際檢查頁面就給予內容建議先閱讀真實頁面
籠統的「改善 SEO」輸出將每項建議綁定到特定頁面或資產

相關技能

  • 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.

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