Schema Markup Generator logo

Schema Markup Generator

OrganizationPopular
nowork-studio
schema-markup-generator

Generate JSON-LD structured data markup for rich results in Google Search. Supports FAQ, HowTo, Article, Product, LocalBusiness, and multi-type schemas. Validates against Google requirements and provides implementation guidance. Use when asked to "add schema markup", "generate structured data", "JSON-LD", "rich snippets", "FAQ schema", "product markup", "add structured data to my page", "how to get rich snippets", or any structured data task.

Overview

Publishernowork-studio
Repositorynotfair-plugin
Skill nameschema-markup-generator
Stars
3.8K
Forks
488
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by nowork-studio on GitHub. Read the source before you install it.

Installation

Install the Schema Markup Generator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/nowork-studio/notfair-plugin.git /tmp/notfair-plugin
mkdir -p .claude/skills
cp -r /tmp/notfair-plugin/seo/schema-markup-generator .claude/skills/schema-markup-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Schema Markup Generator in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Schema Markup Generator on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Schema Markup Generator is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Schema Markup Generator

This skill creates Schema.org structured data markup in JSON-LD format to help search engines understand your content and enable rich results in SERPs.

When This Must Trigger

Use this when the conversation involves any of these situations — even if the user does not use SEO terminology:

Use this whenever the task needs a shippable asset or transformation that should feed directly into quality review, deployment, or monitoring.

  • Adding FAQ schema for expanded SERP presence
  • Creating How-To schema for step-by-step content
  • Adding Product schema for e-commerce pages
  • Implementing Article schema for blog posts
  • Adding Local Business schema for location pages
  • Creating Review/Rating schema
  • Implementing Organization schema for brand presence
  • Any page where rich results would improve visibility

What This Skill Does

  1. Schema Type Selection: Recommends appropriate schema types
  2. JSON-LD Generation: Creates valid structured data markup
  3. Property Mapping: Maps your content to schema properties
  4. Validation Guidance: Ensures schema meets requirements
  5. Nested Schema: Handles complex, multi-type schemas
  6. Rich Result Eligibility: Identifies which rich results you can target

Quick Start

Start with one of these prompts.

Generate Schema for Content

Generate schema markup for this [content type]: [content/URL]
Create FAQ schema for these questions and answers: [Q&A list]

Specific Schema Types

Create Product schema for [product name] with [details]
Generate LocalBusiness schema for [business name and details]

Audit Existing Schema

Review and improve this schema markup: [existing schema]

Data Sources

With ~~web crawler connected: Automatically crawl and extract page content (visible text, headings, lists, tables), existing schema markup, page metadata, and structured content elements that map to schema properties.

With manual data only: Ask the user to provide:

  1. Page URL or full HTML content
  2. Page type (article, product, FAQ, how-to, local business, etc.)
  3. Specific data needed for schema (prices, dates, author info, Q&A pairs, etc.)
  4. Current schema markup (if optimizing existing)

Proceed with the full workflow using provided data. Note in the output which data is from automated extraction vs. user-provided data.

Instructions

When a user requests schema markup:

  1. Identify Content Type and Rich Result Opportunity

    Reference the CORE-EEAT Benchmark item O05 (Schema Markup) for content-type to schema mapping:

    markdown
    ### CORE-EEAT Schema Mapping (O05)
    
    | Content Type | Required Schema | Conditional Schema |
    |-------------|----------------|--------------------|
    | Blog (guides) | Article, Breadcrumb | FAQ, HowTo |
    | Blog (tools) | Article, Breadcrumb | FAQ, Review |
    | Blog (insights) | Article, Breadcrumb | FAQ |
    | Alternative | Comparison*, Breadcrumb, FAQ | AggregateRating |
    | Best-of | ItemList, Breadcrumb, FAQ | AggregateRating per tool |
    | Use-case | WebPage, Breadcrumb, FAQ ||
    | FAQ | FAQPage, Breadcrumb ||
    | Landing | SoftwareApplication, Breadcrumb, FAQ | WebPage |
    | Testimonial | Review, Breadcrumb | FAQ, Person |
    
    *Use the mapping above to ensure schema type matches content type (CORE-EEAT O05: Pass criteria).*
    markdown
    ### Schema Analysis
    
    **Content Type**: [blog/product/FAQ/how-to/local business/etc.]
    **Page URL**: [URL]
    
    **Eligible Rich Results**:
    
    | Rich Result Type | Eligibility | Impact |
    |------------------|-------------|--------|
    | FAQ | ✅/❌ | High - Expands SERP presence |
    | How-To | ✅/❌ | Medium - Shows steps in SERP |
    | Product | ✅/❌ | High - Shows price, availability |
    | Review | ✅/❌ | High - Shows star ratings |
    | Article | ✅/❌ | Medium - Shows publish date, author |
    | Breadcrumb | ✅/❌ | Medium - Shows navigation path |
    | Video | ✅/❌ | High - Shows video thumbnail |
    
    **Recommended Schema Types**:
    1. [Primary schema type] - [reason]
    2. [Secondary schema type] - [reason]
  2. Generate Schema Markup

    Based on the identified content type, generate the appropriate JSON-LD schema. Supported types: FAQPage, HowTo, Article/BlogPosting/NewsArticle, Product, LocalBusiness, Organization, BreadcrumbList, Event, Recipe, and combined multi-type schemas.

    Reference: See references/schema-templates.md for complete, copy-ready JSON-LD templates for all schema types with required and optional properties.

    For each schema generated, include:

    • All required properties for the chosen type
    • Rich result preview showing expected SERP appearance
    • Notes on which properties are required vs. optional

    When combining multiple schema types on one page, wrap them in a JSON array inside a single <script type="application/ld+json"> tag.

  3. Provide Implementation and Validation

    markdown
    ## Implementation Guide
    
    ### Adding Schema to Your Page
    
    **Option 1: In HTML <head>**
    ```html
    <head>
      <script type="application/ld+json">
        [Your JSON-LD schema here]
      </script>
    </head>

    Option 2: Before closing

    html
      <script type="application/ld+json">
        [Your JSON-LD schema here]
      </script>
    </body>

    Validation Steps

    1. ~~schema validator

      • Test your live URL or paste code
      • Check for errors and warnings
    2. Schema.org Validator

    3. ~~search console

      • Monitor rich results in ~~search console
      • Check Enhancements reports for issues

    Validation Checklist

    • JSON syntax is valid (no trailing commas)
    • All required properties present
    • URLs are absolute, not relative
    • Dates are in ISO 8601 format
    • Content matches visible page content
    • No policy violations

Validation Checkpoints

Input Validation

  • Page URL or content provided
  • Schema type appropriate for content (Article for blog, Product for e-commerce, etc.)
  • All required data available (author, dates, prices, etc. depending on schema type)
  • Content eligibility for rich results confirmed

Output Validation

  • JSON syntax validates (no trailing commas, proper quotes)
  • All required properties present for chosen schema type
  • URLs are absolute, not relative
  • Dates in ISO 8601 format (YYYY-MM-DDTHH:MM:SS+00:00)
  • Schema content matches visible page content exactly
  • Passes ~~schema validator with no errors
  • Source of each data point clearly stated (~~web crawler extraction, user-provided, or manual entry)

Example

User: "Generate FAQ schema for a page about SEO with 3 questions"

Output:

markdown
## FAQ Schema for SEO Page

### Schema Markup

```json
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is SEO?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "SEO (Search Engine Optimization) is the practice of optimizing websites and content to rank higher in search engine results pages (SERPs). It involves technical optimizations, content creation, and link building to increase organic visibility and drive qualified traffic to your website."
      }
    },
    {
      "@type": "Question",
      "name": "How long does SEO take to work?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "SEO typically takes 3-6 months to show significant results, though this varies based on competition, domain authority, and effort invested. New websites may take 6-12 months, while established sites with existing authority can see improvements in 1-3 months for less competitive keywords."
      }
    },
    {
      "@type": "Question",
      "name": "Is SEO better than paid advertising?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "SEO and paid advertising serve different purposes. SEO provides sustainable, long-term traffic without per-click costs but takes time to build. Paid advertising delivers immediate results but stops when you stop paying. Most successful businesses use both: paid ads for immediate leads and SEO for long-term growth."
      }
    }
  ]
}

Implementation: Wrap the above JSON-LD in <script type="application/ld+json">...</script> and place in <head> or before </body>. Test with ~~schema validator.

SERP Preview

SEO Guide: Complete Beginner's Tutorial
yoursite.com/seo-guide/
Learn SEO from scratch with our comprehensive guide...

▼ What is SEO?
  SEO (Search Engine Optimization) is the practice of optimizing...
▼ How long does SEO take to work?
  SEO typically takes 3-6 months to show significant results...
▼ Is SEO better than paid advertising?
  SEO and paid advertising serve different purposes...

## Schema Type Quick Reference

| Content Type | Schema Type | Key Properties |
|--------------|-------------|----------------|
| Blog Post | BlogPosting/Article | headline, datePublished, author |
| Product | Product | name, price, availability |
| FAQ | FAQPage | Question, Answer |
| How-To | HowTo | step, totalTime |
| Local Business | LocalBusiness | address, geo, openingHours |
| Recipe | Recipe | ingredients, cookTime |
| Event | Event | startDate, location |
| Video | VideoObject | uploadDate, duration |
| Course | Course | provider, name |
| Review | Review | itemReviewed, ratingValue |

## Tips for Success

1. **Match visible content** - Schema must reflect what users see
2. **Don't spam** - Only add schema for relevant content
3. **Keep updated** - Update dates and prices when they change
4. **Test thoroughly** - Validate before deploying
5. **Monitor Search Console** - Watch for errors and warnings

## Schema Type Decision Tree

> **Reference**: See [references/schema-decision-tree.md](references/schema-decision-tree.md) for the full decision tree (content-to-schema mapping), industry-specific recommendations, implementation priority tiers (P0-P4), and validation quick reference.


## Reference Materials

- [Schema Templates](references/schema-templates.md) - Copy-ready JSON-LD templates for all schema types
- [Validation Guide](references/validation-guide.md) - Common errors, required properties, testing workflow

## Next Best Skill

- **Primary**: [seo-analysis](../seo-analysis/SKILL.md) — verify implementation with a technical SEO audit.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Schema Markup Generator AI skill do?

Generate JSON-LD structured data markup for rich results in Google Search. Supports FAQ, HowTo, Article, Product, LocalBusiness, and multi-type schemas. Validates against Google requirements and provides implementation guidance. Use when asked to "add schema markup", "generate structured data", "JSON-LD", "rich snippets", "FAQ schema", "product markup", "add structured data to my page", "how to get rich snippets", or any structured data task.

Why use Schema Markup Generator on TypingMind?

Because you install it once and use it with any model. Schema Markup Generator is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Schema Markup Generator in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nowork-studio/notfair-plugin/tree/main/seo/schema-markup-generator. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Schema Markup Generator?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Schema Markup Generator?

As many as you like. As long as a model supports skills, you can use Schema Markup Generator with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Schema Markup Generator AI skill free?

Yes. It is published on GitHub by nowork-studio under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇