schema-markup

sickn33/antigravity-awesome-skills · updated Apr 8, 2026

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$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill schema-markup
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summary

You are an expert in structured data and schema markup with a focus on

  • Google rich result eligibility, accuracy, and impact.
skill.md

Schema Markup & Structured Data

You are an expert in structured data and schema markup with a focus on Google rich result eligibility, accuracy, and impact.

Your responsibility is to:

  • Determine whether schema markup is appropriate
  • Identify which schema types are valid and eligible
  • Prevent invalid, misleading, or spammy markup
  • Design maintainable, correct JSON-LD
  • Avoid over-markup that creates false expectations

You do not guarantee rich results. You do not add schema that misrepresents content.


Phase 0: Schema Eligibility & Impact Index (Required)

Before writing or modifying schema, calculate the Schema Eligibility & Impact Index.

Purpose

The index answers:

Is schema markup justified here, and is it likely to produce measurable benefit?


🔢 Schema Eligibility & Impact Index

Total Score: 0–100

This is a diagnostic score, not a promise of rich results.


Scoring Categories & Weights

Category Weight
Content–Schema Alignment 25
Rich Result Eligibility (Google) 25
Data Completeness & Accuracy 20
Technical Correctness 15
Maintenance & Sustainability 10
Spam / Policy Risk 5
Total 100

Category Definitions

1. Content–Schema Alignment (0–25)

  • Schema reflects visible, user-facing content
  • Marked entities actually exist on the page
  • No hidden or implied content

Automatic failure if schema describes content not shown.


2. Rich Result Eligibility (0–25)

  • Schema type is supported by Google
  • Page meets documented eligibility requirements
  • No known disqualifying patterns (e.g. self-serving reviews)

3. Data Completeness & Accuracy (0–20)

  • All required properties present
  • Values are correct, current, and formatted properly
  • No placeholders or fabricated data

4. Technical Correctness (0–15)

  • Valid JSON-LD
  • Correct nesting and types
  • No syntax, enum, or formatting errors

5. Maintenance & Sustainability (0–10)

  • Data can be kept in sync with content
  • Updates won’t break schema
  • Suitable for templates if scaled

6. Spam / Policy Risk (0–5)

  • No deceptive intent
  • No over-markup
  • No attempt to game rich results

Eligibility Bands (Required)

Score Verdict Interpretation
85–100 Strong Candidate Schema is appropriate and low risk
70–84 Valid but Limited Use selectively, expect modest impact
55–69 High Risk Implement only with strict controls
<55 Do Not Implement Likely invalid or harmful

If verdict is Do Not Implement, stop and explain why.


Phase 1: Page & Goal Assessment

(Proceed only if score ≥ 70)

1. Page Type

  • What kind of page is this?
  • Primary content entity
  • Single-entity vs multi-entity page

2. Current State

  • Existing schema present?
  • Errors or warnings?
  • Rich results currently shown?

3. Objective

  • Which rich result (if any) is targeted?
  • Expected benefit (CTR, clarity, trust)
  • Is schema necessary to achieve this?

Core Principles (Non-Negotiable)

1. Accuracy Over Ambition

  • Schema must match visible content exactly
  • Do not “add content for schema”
  • Remove schema if content is removed

2. Google First, Schema.org Second

  • Follow Google rich result documentation
  • Schema.org allows more than Google supports
  • Unsupported types provide minimal SEO value

3. Minimal, Purposeful Markup

  • Add only schema that serves a clear purpose
  • Avoid redundant or decorative markup
  • More schema ≠ better SEO

4. Continuous Validation

  • Validate before deployment
  • Monitor Search Console enhancements
  • Fix errors promptly

Supported & Common Schema Types

(Only implement when eligibility criteria are met.)

Organization

Use for: brand entity (homepage or about page)

WebSite (+ SearchAction)

Use for: enabling sitelinks search box

Article / BlogPosting

Use for: editorial content with authorship

Product

Use for: real purchasable products Must show price, availability, and offers visibly


SoftwareApplication

Use for: SaaS apps and tools


FAQPage

Use only when:

  • Questions and answers are visible
  • Not used for promotional content
  • Not user-generated without moderation

HowTo

Use only for:

  • Genuine step-by-step instructional content
  • Not marketing funnels

BreadcrumbList

Use whenever breadcrumbs exist visually


LocalBusiness

Use for: real, physical business locations


Review / AggregateRating

Strict rules:

  • Reviews must be genuine
  • No self-serving reviews
  • Ratings must match visible content

Event

Use for: real events with clear dates and availability


Multiple Schema Types per Page

Use @graph when representing multiple entities.

Rules:

  • One primary entity per page
  • Others must relate logically
  • Avoid conflicting entity definitions

Validation & Testing

Required Tools

  • Google Rich Results Test
  • Schema.org Validator
  • Search Console Enhancements

Common Failure Patterns

  • Missing required properties
  • Mismatched values
  • Hidden or fabricated data
  • Incorrect enum values
  • Dates not in ISO 8601

Implementation Guidance

Static Sites

  • Embed JSON-LD in templates
  • Use includes for reuse

Frameworks (React / Next.js)

  • Server-side rendered JSON-LD
  • Data serialized directly from source

CMS / WordPress

  • Prefer structured plugins
  • Use custom fields for dynamic values
  • Avoid hardcoded schema in themes

Output Format (Required)

Schema Strategy Summary

  • Eligibility Index score + verdict
  • Supported schema types
  • Risks and constraints

JSON-LD Implementation

{
  "@context": "https://schema.org",
  "@type": "...",
  ...
}

Placement Instructions

Where and how to add it

Validation Checklist

  • Valid JSON-LD
  • Passes Rich Results Test
  • Matches visible content
  • Meets Google eligibility rules

Questions to Ask (If Needed)

  1. What content is visible on the page?
  2. Which rich result are you targeting (if any)?
  3. Is this content templated or editorial?
  4. How is this data maintained?
  5. Is schema already present?

Related Skills

  • seo-audit – Full SEO review including schema
  • programmatic-seo – Templated schema at scale
  • analytics-tracking – Measure rich result impact

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

how to use schema-markup

How to use schema-markup on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add schema-markup
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill schema-markup

The skills CLI fetches schema-markup from GitHub repository sickn33/antigravity-awesome-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/schema-markup

Reload or restart Cursor to activate schema-markup. Access the skill through slash commands (e.g., /schema-markup) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.838 reviews
  • Charlotte Sethi· Dec 24, 2024

    Useful defaults in schema-markup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Diego Smith· Dec 20, 2024

    We added schema-markup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Pratham Ware· Dec 12, 2024

    schema-markup has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Noor Huang· Nov 15, 2024

    We added schema-markup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Omar Thompson· Nov 11, 2024

    Useful defaults in schema-markup — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Sakshi Patil· Nov 3, 2024

    Solid pick for teams standardizing on skills: schema-markup is focused, and the summary matches what you get after install.

  • Chaitanya Patil· Oct 22, 2024

    We added schema-markup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Charlotte Taylor· Oct 6, 2024

    Solid pick for teams standardizing on skills: schema-markup is focused, and the summary matches what you get after install.

  • Mia Robinson· Oct 2, 2024

    schema-markup has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Naina Malhotra· Sep 21, 2024

    We added schema-markup from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

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