Agent skill

Web Content

by alinaqi in alinaqi/maggy

SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization

MITAuto-check passedMarketing & SEO

Install Web Content

skills CLI
$ npx skills add alinaqi/maggy --skill web-content -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install alinaqi/maggy web-content --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/alinaqi/maggy.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/web-content .claude/skills/web-content && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
web-content
GitHub stars
707
Token cost
~3.8k tokens
SKILL.md length
488 words
Files
1
Skills in repo
71
Repo updated
First seen
Licence
MIT

At a glance

SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization

  • Works in 7 steps: TL;DR summaries → Definition boxes ("What is X?") → Comparison tables → …
  • Tasks that involve Web search
  • SKILL.md covers Philosophy, Content Structure for AI + SEO, Page Types & Templates and AI-Optimized Content Formats, plus 3 more sections
  • Reaches schema.org and twitter.com

What it does

Web Content is an agent skill from alinaqi/maggy. SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization

Its SKILL.md is about 3.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Web search, AI search optimization and Schema markup. It works with OpenAI and Perplexity. The repository describes itself as: What started as an opinionated Claude Code setup kit is now an autonomous AI engineering command center. The licence is MIT.

When your agent uses it

  • Tasks that involve Web search
  • Tasks that involve AI search optimization
  • Tasks that involve Schema markup

Example prompts

  • “/web-content”

Workflow steps

7 steps, taken from the first numbered list in SKILL.md.

  1. TL;DR summaries
  2. Definition boxes ("What is X?")
  3. Comparison tables
  4. Step-by-step guides
  5. FAQ sections
  6. Stat boxes with sources
  7. Listicles with numbers

What it can do on your machine

Read from SKILL.md and the folder at commit 72a456e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown, json, html, regex, javascript and bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • schema.org
    • twitter.com
    • linkedin.com
    • github.com
    • validator.schema.org
    • search.google.com

    Also links to:

    • skale.so
    • gravitatedesign.com
    • surferseo.com
    • siddharthbharath.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Web Content loads about 3.8k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 488 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~3.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from alinaqi/maggy at commit 72a456e, republished under its MIT licence (© alinaqi). 488 words, ~3,793 tokens.

Download SKILL.mdSave it as .claude/skills/web-content/SKILL.md (or your agent's skills folder).
name
web-content
description
SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization
when-to-use
When creating web content that needs SEO and AI discoverability
user-invocable
false
effort
medium

Web Content Skill

For creating web content optimized for both traditional SEO and AI discovery (ChatGPT, Perplexity, Claude, Gemini).

Sources: GEO Complete Guide | AI Search SEO | LLM Optimization | Generative Engine Optimization


Philosophy

SEO gets clicks. GEO gets citations.

Traditional SEO optimizes for Google rankings. Generative Engine Optimization (GEO) optimizes for being cited by AI assistants. Modern content needs both:

  • SEO: Rank on search results pages
  • GEO: Be cited in AI-generated answers (ChatGPT, Perplexity, Claude, Gemini)

AI traffic grew 1,200% between July 2024 and February 2025. Google's search share dropped below 90% for the first time in a decade. Optimize for both.


Content Structure for AI + SEO

The Golden Rule

Write for humans, structure for machines.

AI systems prefer:

  • Short, clear, fact-based content
  • Clean formatting (headers, bullets, tables)
  • Standalone sections that can be quoted
  • Direct answers to questions

Page Types & Templates

Homepage
markdown
## Homepage Structure

### Above the Fold
- **Headline**: Clear value proposition (what you do + for whom)
- **Subheadline**: How you deliver that value
- **Primary CTA**: One clear action
- **Trust signals**: Logos, testimonials, stats

### Content Sections
1. **Problem Statement**: Pain point you solve
2. **Solution Overview**: How you solve it (3-4 key features)
3. **Social Proof**: Testimonials, case studies, logos
4. **How It Works**: 3-step process (simple)
5. **Pricing Preview**: Or link to pricing page
6. **FAQ Section**: 5-7 common questions (GEO gold)
7. **Final CTA**: Repeat primary action

### Schema Required
- Organization schema (name, logo, founding date, social links)
- WebSite schema with SearchAction
- FAQ schema for questions section
Product/Service Page
markdown
## Product Page Structure

### Hero Section
- **Product Name**: Clear, descriptive
- **One-line Description**: What it does in 10 words or less
- **Key Benefit**: Primary value proposition
- **CTA**: Buy/Try/Demo

### Content Sections
1. **TL;DR Box**: 3-5 bullet summary (AI-quotable)
2. **Problem → Solution**: What problem, how solved
3. **Features Grid**: 4-6 features with icons
4. **Comparison Table**: vs. alternatives (GEO loves these)
5. **Use Cases**: Who uses it and how
6. **Testimonials**: Real names, photos, companies
7. **Pricing**: Clear tiers if applicable
8. **FAQ**: Product-specific questions

### Schema Required
- Product schema (name, description, price, availability)
- Review schema (aggregate rating)
- FAQ schema
- BreadcrumbList schema
Blog Post / Article
markdown
## Blog Post Structure

### Opening (First 100 words)
- **TL;DR**: Direct answer to the title's question
- **What you'll learn**: Bullet list of takeaways
- This section should be quotable standalone

### Body Structure
- **H2 sections**: Main topics (5-7 per article)
- **H3 subsections**: Supporting points
- **Bullet lists**: For scanability
- **Stat boxes**: Highlight key numbers
- **Comparison tables**: When comparing options

### Content Elements
- Definition boxes ("What is X?")
- Step-by-step instructions
- Code examples (if technical)
- Original statistics/research
- Expert quotes with attribution

### Closing
- **Summary**: Key takeaways (bulleted)
- **Next steps**: What reader should do
- **Related content**: Internal links

### Metadata Required
- Author name + bio + photo
- Publication date
- Last updated date (visible!)
- Reading time
- Article schema with author
FAQ Page
markdown
## FAQ Page Structure

### Organization
- Group questions by category
- Most common questions first
- Direct, concise answers
- Link to detailed pages for more info

### Question Format
Q: [Exact question users ask]
A: [Direct answer in first sentence, then elaboration]

### Schema Required
- FAQPage schema (critical for AI discovery)
- Each Q&A as Question/Answer schema
Landing Page
markdown
## Landing Page Structure

### Single Focus
- One offer
- One audience
- One CTA (repeated)

### Sections
1. **Headline**: Benefit-focused, specific
2. **Problem Agitation**: Pain points
3. **Solution**: Your offer
4. **Proof**: Testimonials, stats, logos
5. **Features**: 3-5 key benefits
6. **Objection Handling**: FAQ or guarantee
7. **CTA**: Clear, urgent

### No Navigation
- Remove header nav (reduce exits)
- Single path: read → convert

AI-Optimized Content Formats

TL;DR Boxes
html
<div class="tldr-box">
  <h3>TL;DR</h3>
  <ul>
    <li>Key point 1 with specific detail</li>
    <li>Key point 2 with number/stat</li>
    <li>Key point 3 with actionable insight</li>
  </ul>
</div>

Place at top of articles. AI systems extract these for summaries.

Definition Blocks
markdown
## What is [Term]?

[Term] is [concise definition in one sentence]. It [what it does] by [how it works].

**Key characteristics:**
- Characteristic 1
- Characteristic 2
- Characteristic 3

Start with "What is X?" - AI systems look for this pattern.

Comparison Tables
markdown
| Feature | Product A | Product B | Our Product |
|---------|-----------|-----------|-------------|
| Price | $99/mo | $149/mo | $79/mo |
| Feature 1 | ✓ | ✗ | ✓ |
| Feature 2 | ✗ | ✓ | ✓ |
| Best For | Enterprise | Startups | SMBs |

AI loves structured comparisons. Include in product and review pages.

Stat Boxes
html
<div class="stat-box">
  <span class="stat-number">73%</span>
  <span class="stat-label">of users prefer AI search for complex queries</span>
  <span class="stat-source">Source: Adobe Analytics, 2024</span>
</div>

Original statistics with sources get cited by AI.

Step-by-Step Guides
markdown
## How to [Do Thing]

### Step 1: [Action Verb] [Object]
[Explanation of what to do]

**Example:**
[Concrete example]

### Step 2: [Action Verb] [Object]
[Explanation]

### Step 3: [Action Verb] [Object]
[Explanation]

**Result:** [What user achieves]

Use HowTo schema markup for these.


Schema Markup (Critical for AI)

Organization Schema
json
{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "Your Company",
  "url": "https://yoursite.com",
  "logo": "https://yoursite.com/logo.png",
  "foundingDate": "2020",
  "description": "One sentence description",
  "sameAs": [
    "https://twitter.com/yourcompany",
    "https://linkedin.com/company/yourcompany",
    "https://github.com/yourcompany"
  ],
  "contactPoint": {
    "@type": "ContactPoint",
    "email": "hello@yoursite.com",
    "contactType": "customer service"
  }
}
Article Schema
json
{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article Title",
  "description": "Meta description",
  "image": "https://yoursite.com/article-image.jpg",
  "author": {
    "@type": "Person",
    "name": "Author Name",
    "url": "https://yoursite.com/team/author-name",
    "jobTitle": "Role at Company",
    "sameAs": [
      "https://linkedin.com/in/author",
      "https://twitter.com/author"
    ]
  },
  "publisher": {
    "@type": "Organization",
    "name": "Your Company",
    "logo": {
      "@type": "ImageObject",
      "url": "https://yoursite.com/logo.png"
    }
  },
  "datePublished": "2025-01-15",
  "dateModified": "2025-01-20"
}
FAQ Schema
json
{
  "@context": "https://schema.org",
  "@type": "FAQPage",
  "mainEntity": [
    {
      "@type": "Question",
      "name": "What is your product?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Direct answer here. Keep concise but complete."
      }
    },
    {
      "@type": "Question",
      "name": "How much does it cost?",
      "acceptedAnswer": {
        "@type": "Answer",
        "text": "Pricing starts at $X/month for basic plan..."
      }
    }
  ]
}
Product Schema
json
{
  "@context": "https://schema.org",
  "@type": "Product",
  "name": "Product Name",
  "description": "Product description",
  "image": "https://yoursite.com/product.jpg",
  "brand": {
    "@type": "Brand",
    "name": "Your Company"
  },
  "offers": {
    "@type": "Offer",
    "price": "29.99",
    "priceCurrency": "USD",
    "availability": "https://schema.org/InStock"
  },
  "aggregateRating": {
    "@type": "AggregateRating",
    "ratingValue": "4.8",
    "reviewCount": "127"
  }
}
HowTo Schema
json
{
  "@context": "https://schema.org",
  "@type": "HowTo",
  "name": "How to Set Up Your Account",
  "description": "Step-by-step guide to getting started",
  "step": [
    {
      "@type": "HowToStep",
      "name": "Create account",
      "text": "Go to signup page and enter your email"
    },
    {
      "@type": "HowToStep",
      "name": "Verify email",
      "text": "Click the link in the verification email"
    }
  ]
}

Platform-Specific Optimization

ChatGPT Optimization
markdown
✅ DO:
- TL;DR sections at top of articles
- Consistent formatting (headers, bullets)
- Named authors with credentials
- Original research and statistics
- Multi-intent content (covers related questions)

❌ AVOID:
- Thin content without substance
- Missing author attribution
- Outdated information (no dates)
Perplexity Optimization
markdown
✅ DO:
- Original statistics with sources
- Comparison tables and structured data
- Clean URL slugs (/topic-name not /p=123)
- Short, declarative statements
- Images, charts, diagrams
- YouTube videos (Perplexity shows these)

❌ AVOID:
- Generic content without unique insights
- Missing citations/sources
- Poor URL structure
Claude Optimization
markdown
✅ DO:
- Well-structured, logical content
- Clear definitions and explanations
- Technical accuracy
- Balanced perspectives
- Proper citations

❌ AVOID:
- Misleading or sensational content
- Missing context
- Outdated technical information
Gemini Optimization
markdown
✅ DO:
- Rich schema markup
- Detailed image alt-text
- YouTube content (Google-owned)
- Multimedia (video, audio with transcripts)

❌ AVOID:
- Missing structured data
- Images without alt-text
- Text-only content

E-E-A-T for AI Discovery

Experience
  • First-person case studies
  • "We tested X and found Y"
  • Original screenshots and data
  • User testimonials with real details
Expertise
  • Author bios with credentials
  • Link to author's other work
  • Industry-specific terminology
  • Technical depth appropriate to topic
Authoritativeness
  • Backlinks from trusted sources
  • Mentions in industry publications
  • Citations from other experts
  • Social proof (followers, engagement)
Show full SKILL.md (197 more words)Show less
Trustworthiness
  • Contact information visible
  • About page with team details
  • Privacy policy and terms
  • Secure site (HTTPS)
  • Accurate, up-to-date info

Content Freshness

Visible Dates (Required)
html
<article>
  <header>
    <h1>Article Title</h1>
    <div class="meta">
      <span class="author">By John Smith</span>
      <span class="published">Published: January 15, 2025</span>
      <span class="updated">Last updated: January 20, 2025</span>
    </div>
  </header>
</article>

AI systems prefer recent content. Show dates prominently.

Update Schedule
Content TypeUpdate Frequency
Product pagesOn feature changes
PricingImmediately on change
Blog postsQuarterly review
StatisticsWhen new data available
GuidesSemi-annually

Analytics for AI Traffic

GA4 Regex Filter
regex
.*chatgpt\.com.*|.*perplexity\.ai.*|.*gemini\.google\.com.*|.*copilot\.microsoft\.com.*|.*openai\.com.*|.*claude\.ai.*|.*poe\.com.*|.*you\.com.*|.*phind\.com.*
Track AI Referrals
javascript
// Check for AI referrer
const aiReferrers = [
  'chatgpt.com',
  'chat.openai.com',
  'perplexity.ai',
  'claude.ai',
  'gemini.google.com',
  'copilot.microsoft.com',
  'poe.com',
  'you.com',
  'phind.com'
];

const referrer = document.referrer;
const isAIReferral = aiReferrers.some(ai => referrer.includes(ai));

if (isAIReferral) {
  analytics.track('ai_referral', {
    source: referrer,
    page: window.location.pathname
  });
}
Survey for AI Discovery

Add to forms:

markdown
How did you hear about us?
- [ ] Google Search
- [ ] ChatGPT
- [ ] Perplexity
- [ ] Claude
- [ ] Social Media
- [ ] Referral
- [ ] Other

Content Checklist

Before Publishing
markdown
## SEO Checklist
- [ ] Title tag (50-60 chars) with primary keyword
- [ ] Meta description (150-160 chars) with CTA
- [ ] URL slug is clean and descriptive
- [ ] H1 matches title intent
- [ ] H2/H3 hierarchy is logical
- [ ] Images have descriptive alt-text
- [ ] Internal links to related content
- [ ] External links to authoritative sources

## GEO Checklist
- [ ] TL;DR or summary at top
- [ ] Direct answer to main question in first paragraph
- [ ] Stat boxes with sources
- [ ] Comparison tables where applicable
- [ ] FAQ section with schema
- [ ] Author name, bio, and credentials
- [ ] Publication and last-updated dates visible
- [ ] Schema markup validated
- [ ] Content can be quoted standalone
- [ ] Original insights or data included
Schema Validation
bash
# Validate schema markup
# Use: https://validator.schema.org/
# Or: https://search.google.com/test/rich-results

Project Structure

project/
├── content/
│   ├── pages/
│   │   ├── home.md
│   │   ├── about.md
│   │   ├── pricing.md
│   │   └── contact.md
│   ├── blog/
│   │   ├── post-1.md
│   │   └── post-2.md
│   └── legal/
│       ├── privacy.md
│       └── terms.md
├── components/
│   ├── SchemaMarkup.tsx
│   ├── TLDRBox.tsx
│   ├── StatBox.tsx
│   ├── FAQSection.tsx
│   └── AuthorBio.tsx
└── lib/
    └── schema.ts           # Schema generators

Anti-Patterns

  • No dates - AI deprioritizes undated content
  • Anonymous content - No author = no E-E-A-T
  • Walls of text - Break up with headers, bullets, boxes
  • Generic content - Add original insights, data, opinions
  • Missing schema - Invisible to structured data crawlers
  • Outdated info - Update quarterly minimum
  • No FAQ - Missing easy GEO win
  • Poor URL structure - Use /topic-name not /p=12345

Quick Reference

Content Formats AI Loves
  1. TL;DR summaries
  2. Definition boxes ("What is X?")
  3. Comparison tables
  4. Step-by-step guides
  5. FAQ sections
  6. Stat boxes with sources
  7. Listicles with numbers
Required Schema by Page Type
Page TypeSchema
HomepageOrganization, WebSite
Blog PostArticle, Author, FAQ
ProductProduct, Review, FAQ
FAQFAQPage
How-toHowTo
AboutOrganization, Person

© alinaqi, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/web-content of alinaqi/maggy.

Open the folder on GitHubat commit 72a456e

Compare with similar skills

Web Content next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Web Content compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Web Content this skillalinaqi/maggy707—~3.8kAutomated safety check: PassMIT
Geoliangdabiao/GEO-Content-Optimizer-Skill2051 repos~2.3kAutomated safety check: NotesMIT
Geo Optimizerliangdabiao/GEO-Content-Optimizer-Skill205—~1.1kAutomated safety check: PassNone
Geo Optimizerhuifer/claude-code-seo110—~650Automated safety check: NotesMIT
SEO Geo OptimizerNeverSight/learn-skills.dev2161 repos~2.6kAutomated safety check: PassMIT
Money SEOiamzifei/show-me-the-money1k—~4.4kAutomated safety check: PassCustom licence

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Categories

Questions about Web Content

What does Web Content do?

SEO and AI discovery (GEO) - schema, ChatGPT/Perplexity optimization. Web Content is an agent skill from alinaqi/maggy.

When should I use Web Content?

Web Content fits situations like: tasks that involve Web search; tasks that involve AI search optimization; tasks that involve Schema markup.

How do I install Web Content in Claude Code?

Run `npx skills add alinaqi/maggy --skill web-content -a claude-code`. Or copy the skill folder (skills/web-content in alinaqi/maggy) into .claude/skills/web-content in your project. Claude Code loads it when a task matches its description.

How do I install Web Content in Codex?

Run `npx skills add alinaqi/maggy --skill web-content -a codex`. Or copy the skill folder (skills/web-content in alinaqi/maggy) into .agents/skills/web-content in your project. Codex loads it when a task matches its description.

Can I use Web Content in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add alinaqi/maggy --skill web-content -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/web-content, .gemini/skills/web-content, .github/skills/web-content and .opencode/skills/web-content in your project.

What does Web Content need to run?

SKILL.md names no scripts, command-line tools or credentials: Web Content is instructions for the agent only.

Does Web Content access the network?

SKILL.md names 10 domains. In commands or code: schema.org, twitter.com, linkedin.com, github.com, validator.schema.org and search.google.com; the agent is likely to contact these when it follows the instructions. As links in the text: skale.so, gravitatedesign.com, surferseo.com and siddharthbharath.com. This is read from the text; nothing was executed.

Is Web Content safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Web Content use?

Web Content is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Web Content use?

About 3.8k tokens (SKILL.md is roughly 15k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Web Content?

Skills that share tags, products or a category with Web Content: Geo (liangdabiao/GEO-Content-Optimizer-Skill, 205 stars), Geo Optimizer (liangdabiao/GEO-Content-Optimizer-Skill, 205 stars), Geo Optimizer (huifer/claude-code-seo, 110 stars) and SEO Geo Optimizer (NeverSight/learn-skills.dev, 216 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Web Content?

alinaqi (a GitHub user) maintains it in alinaqi/maggy, which has 707 GitHub stars. The repository holds 71 skills in this directory. The repository was last updated on September 24, 2026.

Source: alinaqi/maggy on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.