Structures the analysis before making a recommendation
The source asks for analysis, classification, ranking, or scoring before it reaches a conclusion or next action.
IntermediateCorey Haines (marketingskills)
Schema Markup
Schema Markup is an ecommerce AI skill for Corey Haines (marketingskills), built for teams working with Codex, Claude Code, OpenClaw. Use it to you are investigating…
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What this Skill does
You are investigating why a product, category, or landing page is missing expected organic traffic. Bring Adding FAQ schema to a knowledge base page for accordion-style rich results and Implementing Product + AggregateRating schema on e-commerce product pages into the same… Start with a small test around “Adding FAQ schema to a knowledge base page for accordion-style rich results”, then check whether “Implementing Product + AggregateRating schema on e-commerce product pages” fits the way your team actually works.
What makes it different
Before you use it
Source file or structured data
This instruction refers to file or tabular input. Prepare the requested file and confirm that the model you use can read it.
No installation needed
Copy and paste into a model chat
- Expand and copy the complete original Skill.md below.
- Open a new conversation in a compatible AI model, then paste it into the chat box.
- Add verified task details, run one low-risk example, and review the result before using it in store operations.
Original Skill.md
The complete source is shown below. Copy it from the top right to use it.
You are an expert in structured data and schema markup. First, assess the page type, current schema state, and which rich results are being targeted. Use JSON-LD format placed in <head> or end of <body>. Implement the appropriate schema type(s): Organization for company pages, Article/BlogPosting for blog posts, Product for product pages, FAQPage for FAQ content, HowTo for tutorials, BreadcrumbList for breadcrumbs, LocalBusiness for local pages, Event for events, SoftwareApplication for SaaS. For multiple types on one page, use the @graph pattern. Ensure accuracy — schema must match visible page content. Validate using Google Rich Results Test before deployment. For dynamic sites, server-side render schema to ensure it's present in static HTML. Include all required properties and relevant recommended ones. Reference the schema-examples.md reference file for complete JSON-LD templates.Get started
Starter prompts for the main use cases—copy and use them directly.
01Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Schema Markup can help you create, localize, or check product content and visual assets. Use this when the input boundary, owner, and one primary measure from factual corrections, editing time, approval rate, and conversion quality are written down.
Show prompt and variablesHide prompt and variables
Start with one real task
Do not begin with a store-wide rollout. Pick one reversible task where Schema Markup can help you create, localize, or check product content and visual assets. Use this when the input boundary, owner, and one primary measure from factual corrections, editing time, approval rate, and conversion quality are written down.
Use the Skill above to help me with this task: Start with one real task.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not begin with a store-wide rollout. Pick one reversible task where Schema Markup can help you create, localize, or check product content and visual assets.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
02Prepare the input and guardrails
Collect only the verified product facts, source images, brand rules, target channel, and prohibited claims needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Show prompt and variablesHide prompt and variables
Prepare the input and guardrails
Collect only the verified product facts, source images, brand rules, target channel, and prohibited claims needed for this test. Remove unrelated personal data and state which actions must never run automatically. Use this when every input has a known source, sensitive fields are minimized, and the approver knows what the trial can read or change.
Use the Skill above to help me with this task: Prepare the input and guardrails.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Collect only the verified product facts, source images, brand rules, target channel, and prohibited claims needed for this test. Remove unrelated personal data and state which actions must never run automatically.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
03Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Schema Markup to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a product-content draft that a responsible operator can inspect, and it stayed inside the approved boundary.
Show prompt and variablesHide prompt and variables
Inspect the source Skill, then run it
Read the source, installation method, and permission notes before adding Schema Markup to a separate test project. Keep commands and Skill text exactly as published. Use this when you have a product-content draft that a responsible operator can inspect, and it stayed inside the approved boundary.
Use the Skill above to help me with this task: Inspect the source Skill, then run it.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Read the source, installation method, and permission notes before adding Schema Markup to a separate test project. Keep commands and Skill text exactly as published.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
04Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when factual corrections, editing time, approval rate, and conversion quality has a pre-test baseline, and errors and exceptions are logged separately.
Show prompt and variablesHide prompt and variables
Review it against a baseline
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time. Use this when factual corrections, editing time, approval rate, and conversion quality has a pre-test baseline, and errors and exceptions are logged separately.
Use the Skill above to help me with this task: Review it against a baseline.
Task details: [TASK_DETAILS]
Constraints or policies to follow: [CONSTRAINTS]
Do not judge the result by fluency. Compare it with source data, the current SOP, and the pre-test baseline; record factual errors, omissions, and editing time.
Return a practical result and clearly flag anything that needs human review.Replace these variables
[TASK_DETAILS]- The facts, context, or source material for this task.
[CONSTRAINTS]- Replace this placeholder with your verified store-specific information.
Corey Haines
Creator of Marketing Skills, an open-source collection of reusable agent skills for content, SEO, conversion, launch, and growth work.
Risk, permissions, and limitations
Review third-party permission scopes before providing store data. Never paste payment credentials, customer passwords, or unnecessary personal data into a model. Outputs must be checked by the operator responsible for the workflow.
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