Schema logo

Schema

CommunityPopular
fcakyon
schema

Use when adding, fixing, or reviewing structured data: JSON-LD for articles, products, FAQs, breadcrumbs, organizations, and local businesses, and what to do when markup doesn't earn a rich result.

Overview

Publisherfcakyon
Repositoryclaude-codex-settings
Skill nameschema
Stars
1.2K
Forks
113
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the 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.
    https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/seo-skills/skills/schema
  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/fcakyon/claude-codex-settings.git /tmp/claude-codex-settings
mkdir -p .claude/skills
cp -r /tmp/claude-codex-settings/plugins/seo-skills/skills/schema .claude/skills/schema
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Schema markup

Structured data tells a search engine what a page is rather than making it rank. Done right it earns a richer result and makes the page easier for an engine to summarize. Done wrong it earns a manual action.

Four rules govern everything below:

  • Mark up only what the page visibly shows. Schema describing content a reader can't see is a spam signal, and it's the single most common reason markup gets penalized rather than ignored.
  • Use JSON-LD, in a <script type="application/ld+json"> in the head or at the end of the body. Google recommends it, and it's the only format you can add without touching the page's markup.
  • Only claim types and properties the engine actually supports for a rich result. Valid schema.org markup with no supported result does nothing for search, which is fine, but don't promise a rich result it can't produce.
  • Validate before it ships, then watch Search Console. Markup that validates can still be wrong about the page.

Choosing the type

TypeUse onRequired
OrganizationHomepage, about pagename, url
WebSiteHomepage, to declare a site searchname, url
Article, BlogPostingPosts and newsheadline, image, datePublished, author
ProductProduct pagesname, image, offers
SoftwareApplicationApp and SaaS pagesname, offers
FAQPageA page with real questions and answersmainEntity
HowToStep-by-step instructionsname, step
BreadcrumbListAny page with breadcrumbsitemListElement
LocalBusinessA location pagename, address
EventEvents and webinarsname, startDate, location

references/schema-examples.md has a complete, valid block for each of these plus a combined @graph and a Next.js pattern. references/required-properties.json is the same table in the form the bundled validator reads, so adding a type means editing both.

When a page needs several types, prefer one @graph array over several separate script tags: entities can then reference each other by @id instead of repeating themselves.

Reviewing existing markup

A fetch of the page shows only server-rendered JSON-LD. CMS SEO plugins commonly inject it client side, so absence in fetched HTML is not absence on the page. Report what the server HTML contained, then send them to the Rich Results Test, which renders JavaScript.

When markup exists but earns nothing, check in this order: a required property missing, a value in the wrong shape (dates must be ISO 8601, URLs absolute, enumerations exact), the type having no rich result to earn, or the markup describing something the page doesn't show.

From this skill directory, run node scripts/validate_schema.mjs <jsonld-file> on any block before you hand it over. It parses the JSON, checks each type against the required properties in references/required-properties.json, and catches the two value shapes that fail most often: a date that isn't ISO 8601 and a relative URL. It's mechanical, so a clean result means the syntax is right and nothing more.

Then point the user at the Rich Results Test at https://search.google.com/test/rich-results for eligibility, and the schema.org validator at https://validator.schema.org/ for correctness against the vocabulary. Those answer different questions from each other and from the tool, so a block can pass one and fail another. None of the three can tell you whether the markup describes what the page actually shows, which is the accuracy rule above and still yours to check.

Handing it over

Give the complete block, ready to paste, with the page's real values filled in rather than placeholders. Say where it goes, name any property you had to leave out and why, and note which rich result it makes the page eligible for, with eligible being the honest word. Google decides whether to show one.

Sources

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 AI skill do?

Use when adding, fixing, or reviewing structured data: JSON-LD for articles, products, FAQs, breadcrumbs, organizations, and local businesses, and what to do when markup doesn't earn a rich result.

Why use Schema on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/seo-skills/skills/schema. 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?

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?

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

Is the Schema AI skill free?

Yes. It is published on GitHub by fcakyon 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 👇