algo-seo-schema
Skill238 starsChanged 6 months agoMITView on GitHub
Implement Schema.org structured data markup in JSON-LD format for enhanced search results. Use this skill when the user needs to add rich snippets to web pages, implement FAQ/Product/Article schema, or validate structured data — even if they say 'rich snippets', 'structured data', or 'Google rich results'.
In this repository315 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- algo-ad-biddingSkill
- algo-ad-budgetSkill
- algo-ad-ctrSkill
- algo-ad-gspSkill
- algo-ad-vcgSkill
- algo-blockchain-basicsSkill
- algo-blockchain-smart-contractSkill
- algo-ecom-bm25Skill
- algo-ecom-rankingSkill
- algo-ecom-searchSkill
- algo-forecast-arimaSkill
- algo-forecast-ensembleSkill
- algo-forecast-exponentialSkill
- algo-forecast-prophetSkill
- algo-hr-compensationSkill
- algo-hr-matchingSkill
- algo-hr-turnoverSkill
- algo-mfg-cpkSkill
- algo-mfg-doeSkill
- algo-mfg-fmeaSkill
- algo-mfg-spcSkill
- algo-net-centralitySkill
- algo-net-communitySkill
- algo-net-influenceSkill
- algo-nlp-ldaSkill
- algo-nlp-nerSkill
- algo-nlp-similaritySkill
- algo-nlp-summarizationSkill
- algo-price-bundleSkill
- algo-price-conjointSkill
- algo-price-dynamicSkill
- algo-price-elasticitySkill
- algo-price-van-westendorpSkill
- algo-rank-bayesianSkill
- algo-rank-eloSkill
- algo-rank-trueskillSkill
- algo-rank-wilsonSkill
- algo-rec-cfSkill
- algo-rec-contentSkill
- algo-rec-hybridSkill
- algo-rec-mfSkill
- algo-rec-sessionSkill
- algo-risk-altman-zSkill
- algo-risk-benfordSkill
- algo-risk-creditSkill
- algo-risk-varSkill
- algo-sc-bullwhipSkill
- algo-sc-eoqSkill
- algo-sc-newsvendorSkill
- algo-sc-routingSkill
- algo-sc-safety-stockSkill
- algo-seo-backlinkSkill
- algo-seo-contentSkill
- algo-seo-crawlSkill
- algo-seo-pagerankSkill
- algo-seo-technicalSkill
- algo-seo-tfidfSkill
- algo-social-engagementSkill
- The first 60 shown.
The file
We split this skill into the rules it gives an agent
4 rules. About 0 are common, 3 are distinctive, and 1 is very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈3
Distinctive · estimate
This author's own choices. Worth a look.
1
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 23
When optimizing page content or keywords (use content SEO)
- Distinctive: line 24
When improving page speed (use Core Web Vitals optimization)
- Very specific: line 44
Place JSON-LD in <script type="application/ld+json"> in <head>
- Distinctive: line 80
Type specificity: Use the most specific type available.
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Schema.org Structured Data
Overview
Schema.org structured data provides machine-readable page context to search engines via JSON-LD. Enables rich results (stars, FAQs, breadcrumbs, product cards) in SERPs. Implementation is O(1) per page — it's a markup task, not computational.
When to Use
Trigger conditions:
- Adding rich snippet eligibility to web pages
- Implementing product, article, FAQ, HowTo, or event markup
- Debugging Google Search Console structured data errors
When NOT to use:
- When optimizing page content or keywords (use content SEO)
- When improving page speed (use Core Web Vitals optimization)
Algorithm
IRON LAW: Schema Markup Must MATCH Visible Content
Marking up content that users can't see violates Google guidelines
and risks manual penalties. Every structured data field must
correspond to content visible on the page.Phase 1: Input Validation
Identify page type (Article, Product, FAQ, HowTo, Event, etc.). Map visible content to required and recommended schema properties. Gate: Page type identified, all required properties have visible content.
Phase 2: Core Algorithm
- Select the correct Schema.org type from the vocabulary
- Map page content to schema properties (name, description, image, etc.)
- Build JSON-LD object with @context and @type
- Handle nested types (e.g., Product contains Offer contains Price)
- Place JSON-LD in
<script type="application/ld+json">in<head>
Phase 3: Verification
Validate with Google Rich Results Test. Check: no errors, all required fields present, no mismatch with visible content. Gate: Passes Google Rich Results Test with zero errors.
Phase 4: Output
Return complete JSON-LD markup ready for insertion.
Output Format
{
"schema": {"@context": "https://schema.org", "@type": "Product", "name": "...", "offers": {"@type": "Offer", "price": "29.99", "priceCurrency": "TWD"}},
"validation": {"errors": 0, "warnings": 1, "eligible_rich_results": ["Product snippet"]}
}Examples
Sample I/O
Input: FAQ page with 3 questions and answers Expected: FAQPage schema with 3 Question/Answer pairs in JSON-LD
Edge Cases
| Input | Expected | Why |
| Page with no clear type | Use WebPage as fallback | Most generic valid type |
| Multiple schemas needed | Array of JSON-LD objects | One page can have multiple types |
| Missing required field | Error, do not generate | Incomplete schema hurts more than none |
Gotchas
- Required vs recommended: Google requires certain fields per type. Missing required fields = schema ignored entirely. Check documentation per type.
- Nesting depth: Deeply nested schemas (Product > Offer > Seller > Address) are error-prone. Validate each nesting level.
- Schema spam: Adding schema for content not on the page (fake reviews, unavailable prices) triggers manual actions.
- Type specificity: Use the most specific type available. "Article" is better than "WebPage"; "NewsArticle" is better than "Article" for news content.
- Testing gap: Google Rich Results Test shows what Google sees, but not all valid schema triggers rich results. Eligibility ≠ guarantee of display.
References
- For complete property reference by type, see
references/type-properties.md - For common validation errors and fixes, see
references/validation-errors.md
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

