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schema-markup模式标记

Agent Skill

schema-markup 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

10,034

周安装

402

GitHub Stars

35,727

下载量

3,248
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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

3

许可证

MIT

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:schema-markup(模式标记)
来源仓库:https://github.com/sickn33/antigravity-awesome-skills
仓库路径:skills/schema-markup
安装命令:
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill schema-markup
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。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill schema-markup

简介

schema-markup 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围、维护状态,注意是否触发联网、命令执行或文件读写。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

Schema Markup & Structured Data

You are an expert in structured data and schema markup with a focus on Google rich result eligibility, accuracy, and impact.

Your responsibility is to:

  • Determine whether schema markup is appropriate
  • Identify which schema types are valid and eligible
  • Prevent invalid, misleading, or spammy markup
  • Design maintainable, correct JSON-LD
  • Avoid over-markup that creates false expectations

You do not guarantee rich results. You do not add schema that misrepresents content.


Phase 0: Schema Eligibility & Impact Index (Required)

Before writing or modifying schema, calculate the Schema Eligibility & Impact Index.

Purpose

The index answers:

Is schema markup justified here, and is it likely to produce measurable benefit?

🔢 Schema Eligibility & Impact Index

Total Score: 0–100

This is a diagnostic score, not a promise of rich results.


Scoring Categories & Weights

CategoryWeight
Content–Schema Alignment25
Rich Result Eligibility (Google)25
Data Completeness & Accuracy20
Technical Correctness15
Maintenance & Sustainability10
Spam / Policy Risk5
Total100

Category Definitions

1. Content–Schema Alignment (0–25)

  • Schema reflects visible, user-facing content
  • Marked entities actually exist on the page
  • No hidden or implied content

Automatic failure if schema describes content not shown.


2. Rich Result Eligibility (0–25)

  • Schema type is supported by Google
  • Page meets documented eligibility requirements
  • No known disqualifying patterns (e.g. self-serving reviews)

3. Data Completeness & Accuracy (0–20)

  • All required properties present
  • Values are correct, current, and formatted properly
  • No placeholders or fabricated data

4. Technical Correctness (0–15)

  • Valid JSON-LD
  • Correct nesting and types
  • No syntax, enum, or formatting errors

5. Maintenance & Sustainability (0–10)

  • Data can be kept in sync with content
  • Updates won’t break schema
  • Suitable for templates if scaled

6. Spam / Policy Risk (0–5)

  • No deceptive intent
  • No over-markup
  • No attempt to game rich results

Eligibility Bands (Required)

ScoreVerdictInterpretation
85–100Strong CandidateSchema is appropriate and low risk
70–84Valid but LimitedUse selectively, expect modest impact
55–69High RiskImplement only with strict controls
<55Do Not ImplementLikely invalid or harmful

If verdict is Do Not Implement, stop and explain why.


Phase 1: Page & Goal Assessment

(Proceed only if score ≥ 70)

1. Page Type

  • What kind of page is this?
  • Primary content entity
  • Single-entity vs multi-entity page

2. Current State

  • Existing schema present?
  • Errors or warnings?
  • Rich results currently shown?

3. Objective

  • Which rich result (if any) is targeted?
  • Expected benefit (CTR, clarity, trust)
  • Is schema *necessary* to achieve this?

Core Principles (Non-Negotiable)

1. Accuracy Over Ambition

  • Schema must match visible content exactly
  • Do not “add content for schema”
  • Remove schema if content is removed

2. Google First, Schema.org Second

  • Follow Google rich result documentation
  • Schema.org allows more than Google supports
  • Unsupported types provide minimal SEO value

3. Minimal, Purposeful Markup

  • Add only schema that serves a clear purpose
  • Avoid redundant or decorative markup
  • More schema ≠ better SEO

4. Continuous Validation

  • Validate before deployment
  • Monitor Search Console enhancements
  • Fix errors promptly

Supported & Common Schema Types

*(Only implement when eligibility criteria are met.)*

Organization

Use for: brand entity (homepage or about page)

WebSite (+ SearchAction)

Use for: enabling sitelinks search box

Article / BlogPosting

Use for: editorial content with authorship

Product

Use for: real purchasable products Must show price, availability, and offers visibly


SoftwareApplication

Use for: SaaS apps and tools


FAQPage

Use only when:

  • Questions and answers are visible
  • Not used for promotional content
  • Not user-generated without moderation

HowTo

Use only for:

  • Genuine step-by-step instructional content
  • Not marketing funnels

BreadcrumbList

Use whenever breadcrumbs exist visually


LocalBusiness

Use for: real, physical business locations


Review / AggregateRating

Strict rules:

  • Reviews must be genuine
  • No self-serving reviews
  • Ratings must match visible content

Event

Use for: real events with clear dates and availability


Multiple Schema Types per Page

Use @graph when representing multiple entities.

Rules:

  • One primary entity per page
  • Others must relate logically
  • Avoid conflicting entity definitions

Validation & Testing

Required Tools

  • Google Rich Results Test
  • Schema.org Validator
  • Search Console Enhancements

Common Failure Patterns

  • Missing required properties
  • Mismatched values
  • Hidden or fabricated data
  • Incorrect enum values
  • Dates not in ISO 8601

Implementation Guidance

Static Sites

  • Embed JSON-LD in templates
  • Use includes for reuse

Frameworks (React / Next.js)

  • Server-side rendered JSON-LD
  • Data serialized directly from source

CMS / WordPress

  • Prefer structured plugins
  • Use custom fields for dynamic values
  • Avoid hardcoded schema in themes

Output Format (Required)

Schema Strategy Summary

  • Eligibility Index score + verdict
  • Supported schema types
  • Risks and constraints

JSON-LD Implementation

{
  "@context": "https://schema.org",
  "@type": "...",
  ...
}

Placement Instructions

Where and how to add it

Validation Checklist

  • Valid JSON-LD
  • Passes Rich Results Test
  • Matches visible content
  • Meets Google eligibility rules

Questions to Ask (If Needed)

  1. What content is visible on the page?
  2. Which rich result are you targeting (if any)?
  3. Is this content templated or editorial?
  4. How is this data maintained?
  5. Is schema already present?

Related Skills

  • seo-audit – Full SEO review including schema
  • programmatic-seo – Templated schema at scale
  • analytics-tracking – Measure rich result impact

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

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

04

需要参考平台分布和安装热度时

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

补充不同宿主或平台的使用分布数据

能力 5

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

Claude Code

27.95%
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21.31%
按下载量换算692

Antigravity

16.82%
按下载量换算546

Gemini CLI

14.42%
按下载量换算468

Cursor

7.82%
按下载量换算254

Codex

3.47%
按下载量换算113

安全审计

Gen Agent Trust Hub

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Socket

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Snyk

通过

权限和风险

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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。

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