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Blog Schema

CommunityPopular
AgriciDaniel
blog-schema

Generate complete JSON-LD schema markup for blog posts with Article/BlogPosting, Person, Organization, BreadcrumbList, ImageObject, and optional FAQPage. Validates against Google requirements and warns about deprecated types. Use when user says "schema", "blog schema", "json-ld", "structured data", "schema markup", "generate schema".

Overview

PublisherAgriciDaniel
Repositoryclaude-blog
Skill nameblog-schema
Stars
2.2K
Forks
362
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by AgriciDaniel on GitHub. Read the source before you install it.

Installation

Install the Blog Schema 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/AgriciDaniel/claude-blog.git /tmp/claude-blog
mkdir -p .claude/skills
cp -r /tmp/claude-blog/skills/blog-schema .claude/skills/blog-schema
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Blog Schema 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 Blog Schema 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 Blog Schema 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.

Blog Schema: JSON-LD Structured Data Generation

Generates complete, validated JSON-LD schema markup for blog posts using the @graph pattern. Combines multiple schema types into a single script tag with stable @id references for entity linking.

Workflow

Step 1: Read Content

Read the blog post and extract all schema-relevant data:

  • Title (headline)
  • Author (name, job title, social links, credentials)
  • Dates (datePublished, dateModified / lastUpdated)
  • Description (meta description)
  • FAQ section (question and answer pairs)
  • Images (cover image URL, dimensions, alt text; inline images)
  • Organization info (site name, URL, logo)
  • Word count (approximate from content length)
  • Tags/categories (for BreadcrumbList category)
  • Slug (from filename or frontmatter)

Step 2: Generate BlogPosting Schema

Complete BlogPosting with recommended properties when applicable:

json
{
  "@type": "BlogPosting",
  "@id": "{siteUrl}/blog/{slug}#article",
  "headline": "Concise post title",
  "description": "Concise page-specific meta description",
  "datePublished": "YYYY-MM-DD",
  "dateModified": "YYYY-MM-DD",
  "author": { "@id": "{siteUrl}/author/{author-slug}#person" },
  "publisher": { "@id": "{siteUrl}#organization" },
  "image": { "@id": "{siteUrl}/blog/{slug}#primaryimage" },
  "mainEntityOfPage": {
    "@type": "WebPage",
    "@id": "{siteUrl}/blog/{slug}"
  },
  "wordCount": 2400,
  "articleBody": "First 200 characters of content as excerpt..."
}

Google's Article structured data docs do not define required Article properties. Include headline, datePublished, author, publisher, and image when applicable, validate with the Rich Results Test, and treat missing fields as warnings unless the target surface requires them. Recommended properties: description, dateModified, mainEntityOfPage, wordCount, articleBody (excerpt).

Step 3: Generate Person Schema

Author schema with stable @id for cross-referencing:

json
{
  "@type": "Person",
  "@id": "{siteUrl}/author/{author-slug}#person",
  "name": "Author Name",
  "jobTitle": "Role or Title",
  "url": "{siteUrl}/author/{author-slug}",
  "sameAs": [
    "https://twitter.com/handle",
    "https://linkedin.com/in/handle",
    "https://github.com/handle"
  ]
}

Optional properties (include when available):

  • alumniOf - Educational institution (Organization type)
  • worksFor - Employer (reference to Organization @id if same entity)

Step 4: Generate Organization Schema

Blog's parent organization entity:

json
{
  "@type": "Organization",
  "@id": "{siteUrl}#organization",
  "name": "Organization Name",
  "url": "{siteUrl}",
  "logo": {
    "@type": "ImageObject",
    "url": "{siteUrl}/logo.png",
    "width": 600,
    "height": 60
  },
  "sameAs": [
    "https://twitter.com/org",
    "https://linkedin.com/company/org",
    "https://github.com/org"
  ]
}

Logo requirements: use a valid crawlable image URL and follow the active Organization and Article documentation for the target surface. Do not invent hard logo dimensions unless the project or current docs require them.

Step 5: Generate BreadcrumbList

Navigation breadcrumb schema showing content hierarchy:

json
{
  "@type": "BreadcrumbList",
  "@id": "{siteUrl}/blog/{slug}#breadcrumb",
  "itemListElement": [
    {
      "@type": "ListItem",
      "position": 1,
      "name": "Home",
      "item": "{siteUrl}"
    },
    {
      "@type": "ListItem",
      "position": 2,
      "name": "Category Name",
      "item": "{siteUrl}/blog/category/{category-slug}"
    },
    {
      "@type": "ListItem",
      "position": 3,
      "name": "Post Title",
      "item": "{siteUrl}/blog/{slug}"
    }
  ]
}

If no category is available, use "Blog" as the second breadcrumb item with {siteUrl}/blog as the URL.

Step 6: Generate FAQPage Entity Schema (Optional)

Extract Q&A pairs from the blog post's FAQ section:

json
{
  "@type": "FAQPage",
  "@id": "{siteUrl}/blog/{slug}#faq",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is the question?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "The complete visible answer text."
      }
    }
  ]
}

Google retired FAQ rich results for all sites on 2026-05-07. FAQPage is not a Google rich-result or generative-AI optimization path, and it earns no SEO or AI-readiness credit. Only emit it when a visible FAQ genuinely helps readers, with at least one valid Question and matching visible answer. Do not pad an answer to a target length or add an FAQ solely for markup.

Do not substitute QAPage. Google supports QAPage for a page focused on one question where users can submit answers. Editorial FAQs, support FAQs, and blog Q&A sections do not meet that model.

Step 7: Generate VideoObject (if videos present)

For each YouTube video embedded in the post, generate a VideoObject schema:

json
{
  "@type": "VideoObject",
  "@id": "{siteUrl}/blog/{slug}#video-{index}",
  "name": "Video title",
  "description": "Video description excerpt (first 200 chars)",
  "thumbnailUrl": "https://img.youtube.com/vi/{videoId}/hqdefault.jpg",
  "uploadDate": "{ISO 8601 date}",
  "contentUrl": "https://www.youtube.com/watch?v={videoId}",
  "embedUrl": "https://www.youtube.com/embed/{videoId}",
  "duration": "PT{M}M{S}S",
  "interactionStatistic": {
    "@type": "InteractionCounter",
    "interactionType": { "@type": "WatchAction" },
    "userInteractionCount": {viewCount}
  }
}

Add each VideoObject to the @graph array. Use #video-1, #video-2 etc. for the @id fragment. Extract video metadata from the embed's noscript fallback or from YouTube Data API if available via blog-google.

Step 7.5: Generate ImageObject

Cover image schema for the post's primary image:

json
{
  "@type": "ImageObject",
  "@id": "{siteUrl}/blog/{slug}#primaryimage",
  "url": "https://cdn.pixabay.com/photo/.../image.jpg",
  "width": 1200,
  "height": 630,
  "caption": "Descriptive caption matching alt text"
}

Image requirements:

  • URL must be crawlable and publicly accessible
  • Width and height should reflect actual image dimensions
  • Caption should match or closely align with the image alt text
  • Preferred dimensions: 1200x630 (OG-compatible) or 1920x1080

Step 8: Validate & Warn

Check per-surface support before recommending schema types:

TypeGoogle Search statusValid entity/context use
HowToNo current Google rich-result experienceValid schema.org type for genuine how-to content
DatasetUsed by Dataset Search, not general Google Search rich resultsValid only for an actual dataset
QAPageSupported for one question with user-submitted answersDo not use for editorial FAQ content
CourseCourse list remains distinct from the retired Course Info experienceUse only when the current Course list documentation and visible content match
ClaimReview, SpecialAnnouncement, Course Info, Estimated Salary, Learning Video, Vehicle ListingFormer Google Search experiences; support was retiredMay remain schema.org-valid, but never recommend them for Google eligibility
PracticeProblemRemoved from Google Search and its documentationDo not recommend for Google eligibility
Sitelinks Search BoxNo dedicated Google Search visual elementGoogle generates sitelinks algorithmically

Validation checks:

  1. All @id references resolve to entities within the @graph
  2. dateModified is equal to or after datePublished
  3. headline is concise. Warn when it may truncate or becomes unclear
  4. description is concise, page-specific, and not duplicated across posts
  5. All URLs are absolute (not relative)
  6. Image dimensions are positive integers
  7. BreadcrumbList positions are sequential starting from 1
  8. If FAQPage is emitted, visible Q&A content exists and includes at least 1 valid Question

Generative AI note: Structured data is not required for Google generative AI search, and there is no special AI schema. Prioritize accurate, visible-content-consistent Article/BlogPosting, Person, Organization, and BreadcrumbList entities. Add ImageObject or VideoObject when the assets exist. FAQPage remains optional reader-facing markup and adds no Google AI advantage.

Step 9: Output

Combine all schemas into a single <script> tag using the @graph pattern:

Security requirement: build the JSON-LD with a real JSON encoder, never string interpolation. Before embedding in HTML, make the JSON text script-safe by escaping closing script sequences and literal less-than characters, for example replace </ with <\/ and < with \u003c. User-controlled fields such as headline, description, author name, image URL, and breadcrumb labels must only enter the block as JSON-encoded values.

html
<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@graph": [
    { "@type": "BlogPosting", ... },
    { "@type": "Person", ... },
    { "@type": "Organization", ... },
    { "@type": "BreadcrumbList", ... },
    { "@type": "FAQPage", ... },
    { "@type": "VideoObject", ... },
    { "@type": "ImageObject", ... }
  ]
}
</script>

@graph pattern benefits:

  • Single script tag instead of multiple - cleaner HTML
  • Entity linking via stable @id references (e.g., author references Person by @id)
  • Google and AI systems parse @graph arrays correctly
  • Easier to maintain and update as a single block

Output options:

  • Embedded HTML - Ready to paste into <head> or before </body>
  • Standalone JSON - For CMS schema fields or API injection
  • MDX component - If the project uses MDX, wrap in a component

Save the generated schema to the blog post file or to a separate schema file as the user prefers.

Google can process JSON-LD generated by JavaScript when it is present in the rendered DOM. Server-rendered markup is still more portable for non-Google crawlers, but source-only JSON-LD is not a Google requirement. For dynamic markup, validate the rendered URL, confirm the values match visible content, and avoid delayed or failed client requests that leave the rendered DOM empty.

Frequently asked questions

What does the Blog Schema AI skill do?

Generate complete JSON-LD schema markup for blog posts with Article/BlogPosting, Person, Organization, BreadcrumbList, ImageObject, and optional FAQPage. Validates against Google requirements and warns about deprecated types. Use when user says "schema", "blog schema", "json-ld", "structured data", "schema markup", "generate schema".

Why use Blog Schema on TypingMind?

Because you install it once and use it with any model. Blog Schema 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 Blog Schema in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AgriciDaniel/claude-blog/tree/main/skills/blog-schema. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Blog Schema?

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 Blog Schema?

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

Is the Blog Schema AI skill free?

Yes. It is published on GitHub by AgriciDaniel 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.

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