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Agent Skill · 研究检索

schema-markup(模式标记)

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。

schema-markup 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证为 MIT,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 12,723。

总安装 10,034周安装 402GitHub Stars 35,727下载 3,248核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT

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

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

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。

来源仓库内路径:skills/schema-markup

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill sch…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

schema-markup 怎么安装?

在终端执行 npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill schema-markup。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

schema-markup 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

schema-markup 需要什么权限?

权限级别是需要联网。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。

schema-markup 是免费的吗?

来源标注的许可证是 MIT。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

schema-markup 用的人多吗?

公开数据是累计安装 10,034 次,近一周安装 402 次,下载 3,248 次,GitHub 35,727 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

schema-markup 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 3 个目录同时收录,说明这个 Skill 流传比较广。
  • 许可证明确为 MIT,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
schema-markup

模式标记 · 本页

10,03435,727需要联网MIT通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

平台分布

Claude Code

27.95%
按下载量换算908

OpenCode

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

通过

Socket

通过

Snyk

通过

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。

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