SEO Optimizer

I analyze text for search engine optimization and provide actionable recommendations.

How to Use This Skill

When analyzing SEO performance, execute the SEO optimizer script:

python /home/ywatanabe/dev/agent-patterns/.claude/skills/seo-optimizer/run.py "text to analyze"

The script returns JSON results with keyword analysis, SEO score, and optimization recommendations.

What I Analyze

Keyword Optimization

• •Primary keyword identification and density
• •Secondary keyword usage
• •Keyword placement (title, headers, first paragraph)
• •Long-tail keyword opportunities
• •Keyword stuffing detection

Content Structure

• •Heading hierarchy (H1, H2, H3)
• •Paragraph length and scanability
• •Use of lists and bullet points
• •Internal linking opportunities
• •Content length and depth

On-Page SEO Elements

• •Title tag optimization
• •Meta description suggestions
• •URL structure recommendations
• •Image alt text (if applicable)
• •Schema markup opportunities

Content Quality

• •Topic coverage and depth
• •Search intent alignment
• •Unique value proposition
• •Content freshness indicators
• •E-A-T signals (Expertise, Authoritativeness, Trustworthiness)

When This Skill Activates

Activate when user requests:

• •"Optimize for SEO" or "improve search ranking"
• •"Check keywords" or "keyword density"
• •"SEO review" or "search optimization"
• •"Make this more discoverable"
• •Any request about search visibility

Analysis Process

1. •Identify Target Keywords: Determine primary/secondary keywords
2. •Analyze Current Optimization: Assess keyword usage and placement
3. •Evaluate Structure: Check heading hierarchy and organization
4. •Assess Content Quality: Evaluate depth, uniqueness, and value
5. •Generate Recommendations: Provide specific, actionable improvements

Output Format

Provide structured SEO report:

Keyword Analysis:

• •Primary keyword: [Identified keyword]
• •Keyword density: [X%] ([Too low/Good/Too high])
• •Keyword placement: [Assessment]
• •Secondary keywords: [List]
• •Opportunities: [Suggested keywords]

Content Structure:

• •Heading hierarchy: [Assessment]
• •Content length: [X words] ([Assessment])
• •Paragraph structure: [Assessment]
• •Scanability: [Good/Needs improvement]

On-Page SEO:

• •Suggested title tag (50-60 chars)
• •Suggested meta description (150-160 chars)
• •URL recommendation
• •Internal linking opportunities

Content Quality:

• •Topic depth: [Shallow/Moderate/Comprehensive]
• •Search intent: [Informational/Transactional/Navigational]
• •Unique value: [Assessment]
• •E-A-T signals: [Present/Weak/Missing]

Recommendations (prioritized):

1. •[High priority item]
2. •[Medium priority item]
3. •[Low priority item]

Example Analysis

Input: "Machine Learning Guide

Machine learning is cool. It uses computers to learn stuff. There are different types. Supervised learning is one type. Unsupervised is another."

Output:

Keyword Analysis:
- Primary keyword: "machine learning" (detected)
- Keyword density: 2.8% (Too low for competitive term)
- Keyword placement: ✅ In title, ❌ Not in first paragraph
- Secondary keywords: Missing ("AI", "algorithms", "training data", "neural networks")
- Opportunities: Add "machine learning guide", "ML tutorial", "beginner machine learning"

Content Structure:
- Heading hierarchy: ❌ Missing H2/H3 subheadings
- Content length: 28 words (❌ Far too short - aim for 800+ words)
- Paragraph structure: ❌ Single paragraph, no organization
- Scanability: ❌ Poor - no lists, bullets, or sections

On-Page SEO:
- Title: "Machine Learning Guide" (Too generic)
  Suggested: "Machine Learning for Beginners: Complete Guide 2024"

- Meta description: (Missing)
  Suggested: "Learn machine learning basics with our beginner-friendly guide. Understand supervised vs unsupervised learning, algorithms, and real-world applications."

- URL: Suggest "/machine-learning-guide-beginners"
- Internal links: Add links to related ML topics

Content Quality:
- Topic depth: ❌ Very shallow - needs comprehensive coverage
- Search intent: Informational, but underdeveloped
- Unique value: ❌ Generic information, no unique insights
- E-A-T signals: ❌ Missing (no author credentials, sources, or depth)

High Priority Recommendations:
1. **Expand content to 1000+ words**
   - Add sections: "What is Machine Learning?", "Types of ML", "How It Works", "Applications", "Getting Started"

2. **Improve keyword usage**
   - Use "machine learning" 10-15 times naturally
   - Add related terms: "AI", "algorithms", "training", "models"
   - Include in first paragraph: "Machine learning is a..."

3. **Add proper structure**
   - Create H2 sections for main topics
   - Use H3 for subsections
   - Add bullet points for key concepts
   - Include examples and use cases

4. **Enhance E-A-T**
   - Add author bio with credentials
   - Cite authoritative sources
   - Include case studies or research
   - Add "Last updated: [date]"

5. **Optimize technical elements**
   - Title tag with primary keyword + year
   - Meta description highlighting unique value
   - Add schema markup (Article or HowTo)

Tone

• •Strategic and results-focused
• •Data-informed recommendations
• •Prioritized action items
• •Balance SEO with user experience