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algo-seo-schemaalgo SEO schema 搜索

Agent Skill

algo-seo-schema 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

376

周安装

16

GitHub Stars

125

下载量

132
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-schema(algo SEO schema 搜索)
来源仓库:https://github.com/asgard-ai-platform/skills
仓库路径:skills/algo-seo-schema
安装命令:
npx skills add https://github.com/asgard-ai-platform/skills --skill algo-seo-schema
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-schema

简介

algo-seo-schema 提供 Schema.org 结构化数据标记支持,助力富媒体搜索结果展现。

  • 适用于添加产品卡片、FAQ 区块或事件信息以提升点击率的任务。
  • 实现方式为静态标记插入,不影响页面加载性能,复杂度为常数级。
  • 通过 GitHub 安装,使用前应验证是否支持 JSON-LD 格式输出。
  • 不涉及内容优化或速度调整,仅增强搜索引擎对页面的理解维度。

SKILL.md

Schema.org Structured Data

Overview

Schema.org structured data provides machine-readable page context to search engines via JSON-LD. Enables rich results (stars, FAQs, breadcrumbs, product cards) in SERPs. Implementation is O(1) per page — it's a markup task, not computational.

When to Use

Trigger conditions:

  • Adding rich snippet eligibility to web pages
  • Implementing product, article, FAQ, HowTo, or event markup
  • Debugging Google Search Console structured data errors

When NOT to use:

  • When optimizing page content or keywords (use content SEO)
  • When improving page speed (use Core Web Vitals optimization)

Algorithm

IRON LAW: Schema Markup Must MATCH Visible Content
Marking up content that users can't see violates Google guidelines
and risks manual penalties. Every structured data field must
correspond to content visible on the page.

Phase 1: Input Validation

Identify page type (Article, Product, FAQ, HowTo, Event, etc.). Map visible content to required and recommended schema properties. Gate: Page type identified, all required properties have visible content.

Phase 2: Core Algorithm

  1. Select the correct Schema.org type from the vocabulary
  2. Map page content to schema properties (name, description, image, etc.)
  3. Build JSON-LD object with @context and @type
  4. Handle nested types (e.g., Product contains Offer contains Price)
  5. Place JSON-LD in <script type="application/ld+json"> in <head>

Phase 3: Verification

Validate with Google Rich Results Test. Check: no errors, all required fields present, no mismatch with visible content. Gate: Passes Google Rich Results Test with zero errors.

Phase 4: Output

Return complete JSON-LD markup ready for insertion.

Output Format

{
  "schema": {"@context": "https://schema.org", "@type": "Product", "name": "...", "offers": {"@type": "Offer", "price": "29.99", "priceCurrency": "TWD"}},
  "validation": {"errors": 0, "warnings": 1, "eligible_rich_results": ["Product snippet"]}
}

Examples

Sample I/O

Input: FAQ page with 3 questions and answers Expected: FAQPage schema with 3 Question/Answer pairs in JSON-LD

Edge Cases

InputExpectedWhy
Page with no clear typeUse WebPage as fallbackMost generic valid type
Multiple schemas neededArray of JSON-LD objectsOne page can have multiple types
Missing required fieldError, do not generateIncomplete schema hurts more than none

Gotchas

  • Required vs recommended: Google requires certain fields per type. Missing required fields = schema ignored entirely. Check documentation per type.
  • Nesting depth: Deeply nested schemas (Product > Offer > Seller > Address) are error-prone. Validate each nesting level.
  • Schema spam: Adding schema for content not on the page (fake reviews, unavailable prices) triggers manual actions.
  • Type specificity: Use the most specific type available. "Article" is better than "WebPage"; "NewsArticle" is better than "Article" for news content.
  • Testing gap: Google Rich Results Test shows what Google sees, but not all valid schema triggers rich results. Eligibility ≠ guarantee of display.

References

  • For complete property reference by type, see references/type-properties.md
  • For common validation errors and fixes, see references/validation-errors.md

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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

37.74%
按下载量换算50

Claude

26.49%
按下载量换算35

Cursor

20.02%
按下载量换算26

Gemini CLI

8.6%
按下载量换算11

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安装前确认

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