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name: seo-optimizer description: Analyze and optimize text for search engines. Execute Python script for detailed SEO analysis. Use when optimizing for keywords, search visibility, or content structure.

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:

Copypython /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:

CopyKeyword 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