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geo-schema-gen地理模式生成

Agent Skill

geo-schema-gen 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

218

周安装

9

GitHub Stars

10

下载量

71
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:geo-schema-gen(地理模式生成)
来源仓库:https://github.com/geoly-ai/geo-skills
仓库路径:skills/geo-schema-gen
安装命令:
npx skills add https://github.com/geoly-ai/geo-skills --skill geo-schema-gen
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/geoly-ai/geo-skills --skill geo-schema-gen

简介

geo-schema-gen 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中快速定位候选结果。

  • 适用于需要根据关键词或任务场景进行模式生成信息检索的场景。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用该技能。
  • 安装前需确认权限范围和维护状态,注意可能触发的联网或文件操作行为。
  • 适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

SKILL.md

Schema Markup Generator

Methodology by GEOly AI (geoly.ai) — structured data is the language AI uses to understand your brand.

Generate production-ready Schema.org JSON-LD markup for any page type.

Quick Start

Generate schema for your page:

python scripts/generate_schema.py --type <schema-type> [--url <page-url>]

Example:

python scripts/generate_schema.py --type Organization --url example.com
python scripts/generate_schema.py --type FAQPage --file faqs.json

Why Schema Matters for GEO

Structured data helps AI platforms understand:

  • What your content is (entity type)
  • Who created it (author, publisher)
  • When it was published (freshness)
  • How it relates to other content (breadcrumbs)

Without schema, AI systems rely on NLP inference which is less reliable.

Supported Schema Types

TypePriorityBest For
Organization🔴 CriticalHomepage, About page — establishes brand entity
FAQPage🔴 CriticalFAQ/Support pages — feeds AI Q&A answers
Article / BlogPosting🟡 HighBlog posts, news — improves citability
Product🟡 HighProduct/pricing pages — enables shopping citations
HowTo🟡 HighTutorials, guides — feeds step-by-step answers
WebSite🟡 HighHomepage — enables site search in AI
BreadcrumbList🔵 MediumAll pages — improves navigation understanding
VideoObject🔵 MediumVideo pages — enables video citations
ImageObject🔵 MediumImage galleries — enables image citations
LocalBusiness🔵 MediumPhysical locations — local AI search

Full schema reference: See references/schema-types.md

Generation Methods

Method 1: Interactive (Recommended)

python scripts/generate_schema.py --type Organization --interactive

Guided prompts for all required and optional fields.

Method 2: From URL (Auto-Extract)

python scripts/generate_schema.py --type Article --url https://example.com/blog/post

Automatically extracts metadata from the page.

Method 3: From JSON Input

python scripts/generate_schema.py --type FAQPage --file faqs.json

Where faqs.json contains your content data.

Method 4: Batch Generate

python scripts/batch_generate.py sitemap.xml --output schemas/

Generate schemas for all pages in a sitemap.

Validation

Validate generated schema:

python scripts/validate_schema.py schema.json

Checks for:

  • Required fields present
  • Valid Schema.org types
  • Proper JSON-LD syntax
  • Google Rich Results eligibility

Implementation

Add to Your Page

Paste the generated JSON-LD inside your HTML <head>:

<head>
  <script type="application/ld+json">
  {
    "@context": "https://schema.org",
    "@type": "Organization",
    ...
  }
  </script>
</head>

Test Before Deploying

  1. Schema.org Validator: https://validator.schema.org
  2. Google Rich Results Test: https://search.google.com/test/rich-results
  3. JSON-LD Playground: https://json-ld.org/playground/

Common Mistakes

Wrong: Multiple conflicting Organization schemas on same page ✅ Right: One comprehensive Organization schema

Wrong: Using http://schema.org (insecure) ✅ Right: Using https://schema.org (secure)

Wrong: Copy-pasting without updating placeholder values ✅ Right: All fields contain actual, accurate data

Advanced Usage

Multiple Schemas per Page

Some pages need multiple schema types. Combine them in an array:

python scripts/generate_schema.py --types Organization,WebSite --url example.com

Nested Entities

Generate related schemas together:

python scripts/generate_schema.py --type Product \
  --with-offer --with-review --with-brand

Custom Properties

Add custom properties not in the generator:

python scripts/generate_schema.py --type Organization \
  --custom '{"knowsAbout": ["SEO", "AI", "Machine Learning"]}'

Output Formats

  • JSON-LD (default): Ready to paste into HTML
  • JSON: Raw structured data
  • HTML: Complete <script> tag
  • Markdown: With explanations

Schema Hierarchy

Understanding how schemas relate:

Organization (top-level entity)
├── WebSite (belongs to Organization)
├── Product (offered by Organization)
│   ├── Offer (pricing for Product)
│   └── Review (of Product)
├── Article (published by Organization)
│   ├── Author (Person or Organization)
│   └── Publisher (Organization)
└── LocalBusiness (subtype of Organization)
    └── Place (physical location)

See Also

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

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

35.92%
按下载量换算26

Claude

31.65%
按下载量换算22

Cursor

18.96%
按下载量换算13

Gemini CLI

9.17%
按下载量换算7

安全审计

Gen Agent Trust Hub

通过

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

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在 warning/failed 结果,不能写成本站确认安全。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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