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skills/seo-analyzers/SKILL.md
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skills/seo-analyzers/SKILL.md
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---
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name: seo-analyzers
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description: Analyze content quality with Thai language support. Use for keyword density, readability scoring, SEO quality rating (0-100), and AI pattern detection.
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---
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# 🔍 SEO Analyzers - Thai Language Content Analysis
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**Skill Name:** `seo-analyzers`
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**Category:** `quick`
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**Load Skills:** `[]`
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---
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## 🚀 Purpose
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Analyze content quality with full Thai language support:
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- ✅ **Thai keyword density** - PyThaiNLP-based word counting
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- ✅ **Thai readability scoring** - Grade level, formality detection
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- ✅ **Content quality rating** - Overall 0-100 score
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- ✅ **AI pattern detection** - Remove AI watermarks (Thai-aware)
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- ✅ **Search intent analysis** - Classify Thai queries
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**Use Cases:**
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1. Analyze blog post quality before publishing
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2. Check keyword density for Thai content
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3. Score content quality (0-100)
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4. Remove AI patterns from generated content
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5. Analyze search intent for Thai keywords
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---
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## 📋 Pre-Flight Questions
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**MUST ask before analyzing:**
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1. **Content to Analyze:**
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- Text content (paste directly)
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- File path (Markdown, TXT)
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- URL (fetch and analyze)
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2. **Analysis Type:** (Default: All)
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- Keyword density
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- Readability score
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- Quality rating (0-100)
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- AI pattern detection
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- Search intent
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3. **Target Keyword:** (For keyword analysis)
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- Primary keyword
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- Secondary keywords (optional)
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4. **Content Language:** (Auto-detect or specify)
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- Thai
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- English
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- Auto-detect
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---
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## 🔄 Workflows
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### **Workflow 1: Keyword Density Analysis**
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```python
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Input: Article text + target keyword
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Process:
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1. Count Thai words (PyThaiNLP)
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2. Calculate keyword density
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3. Check critical placements (H1, first 100 words, conclusion)
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4. Detect keyword stuffing
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Output:
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- Word count
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- Keyword occurrences
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- Density percentage
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- Status (too_low/optimal/too_high)
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- Recommendations
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```
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### **Workflow 2: Readability Scoring**
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```python
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Input: Article text
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Process:
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1. Count sentences (Thai-aware)
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2. Calculate average sentence length
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3. Detect formality level (Thai particles)
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4. Estimate grade level
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Output:
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- Avg sentence length
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- Grade level (ม.6-ม.12 or 8-10)
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- Formality score (กันเอง/ปกติ/เป็นทางการ)
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- Readability recommendations
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```
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### **Workflow 3: Quality Rating (0-100)**
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```python
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Input: Article text + keyword
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Process:
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1. Keyword optimization (25 points)
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2. Readability (25 points)
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3. Content structure (25 points)
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4. Brand voice alignment (25 points)
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Output:
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- Overall score (0-100)
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- Category breakdowns
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- Priority fixes
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- Publishing readiness status
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```
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### **Workflow 4: AI Pattern Detection**
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```python
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Input: Generated content
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Process:
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1. Remove Unicode watermarks (zero-width spaces)
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2. Replace em-dashes with appropriate punctuation
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3. Detect AI patterns (repetitive structures)
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4. Thai-specific patterns (overly formal language)
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Output:
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- Cleaned content
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- Statistics (chars removed, patterns fixed)
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- AI probability score
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```
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---
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## 🔧 Technical Implementation
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### **Thai Keyword Analyzer:**
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```python
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from pythainlp import word_tokenize
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from pythainlp.util import normalize
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def count_thai_words(text: str) -> int:
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"""Count Thai words accurately (no spaces between words)"""
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tokens = word_tokenize(text, engine="newmm")
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return len([t for t in tokens if t.strip() and not t.isspace()])
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def calculate_density(text: str, keyword: str) -> float:
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"""Calculate keyword density for Thai text"""
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text_norm = normalize(text)
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keyword_norm = normalize(keyword)
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count = text_norm.count(keyword_norm)
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word_count = count_thai_words(text)
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return (count / word_count * 100) if word_count > 0 else 0
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def check_critical_placements(text: str, keyword: str) -> Dict:
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"""Check keyword in critical locations"""
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return {
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'in_first_100_words': keyword in text[:200], # Thai chars are longer
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'in_h1': check_h1(text, keyword),
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'in_conclusion': keyword in text[-500:],
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'density_status': get_density_status(calculate_density(text, keyword))
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}
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```
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### **Thai Readability Scorer:**
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```python
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from pythainlp import sent_tokenize, word_tokenize
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def calculate_thai_readability(text: str) -> Dict:
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"""
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Thai readability scoring (adapted for Thai language)
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Thai doesn't have spaces between words, so we use:
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- Average sentence length (words per sentence)
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- Presence of formal/informal particles
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- Paragraph structure
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"""
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sentences = sent_tokenize(text, engine="whitespace")
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total_words = sum(len(word_tokenize(s, engine="newmm")) for s in sentences)
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avg_sentence_length = total_words / len(sentences) if sentences else 0
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# Detect formality level
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formality = detect_thai_formality(text)
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# Estimate grade level
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if avg_sentence_length < 15:
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grade_level = "ง่าย (ม.6-ม.9)"
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elif avg_sentence_length < 25:
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grade_level = "ปานกลาง (ม.10-ม.12)"
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else:
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grade_level = "ยาก (ม.13+)"
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return {
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'avg_sentence_length': round(avg_sentence_length, 1),
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'grade_level': grade_level,
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'formality': formality,
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'score': calculate_readability_score(avg_sentence_length, formality)
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}
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def detect_thai_formality(text: str) -> str:
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"""
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Detect Thai formality level from particles and word choice
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"""
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formal_particles = ['ครับ', 'ค่ะ', 'ข้าพเจ้า', 'ท่าน', 'ซึ่ง', 'อัน']
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informal_particles = ['นะ', 'จ้ะ', 'อ่ะ', 'มั้ย', 'gue', 'mang']
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formal_count = sum(text.count(p) for p in formal_particles)
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informal_count = sum(text.count(p) for p in informal_particles)
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ratio = formal_count / (formal_count + informal_count) if (formal_count + informal_count) > 0 else 0.5
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if ratio > 0.6:
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return "เป็นทางการ (Formal)"
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elif ratio < 0.4:
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return "กันเอง (Casual)"
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else:
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return "ปกติ (Normal)"
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```
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### **Content Quality Scorer:**
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```python
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def calculate_quality_score(text: str, keyword: str, brand_voice: Dict) -> Dict:
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"""
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Calculate overall content quality score (0-100)
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Categories:
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- Keyword Optimization: 25 points
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- Readability: 25 points
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- Content Structure: 25 points
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- Brand Voice Alignment: 25 points
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"""
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scores = {
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'keyword_optimization': score_keyword_optimization(text, keyword),
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'readability': score_readability(text),
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'structure': score_structure(text),
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'brand_voice': score_brand_voice(text, brand_voice)
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}
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total = sum(scores.values())
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return {
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'overall_score': round(total, 1),
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'categories': scores,
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'status': get_quality_status(total),
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'recommendations': get_quality_recommendations(scores)
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}
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def score_keyword_optimization(text: str, keyword: str) -> float:
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"""Score keyword optimization (0-25)"""
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density = calculate_density(text, keyword)
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placements = check_critical_placements(text, keyword)
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score = 0
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# Density score (10 points)
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if 1.0 <= density <= 1.5:
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score += 10
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elif 0.5 <= density < 1.0 or 1.5 < density <= 2.0:
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score += 5
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# Critical placements (15 points)
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if placements['in_first_100_words']:
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score += 5
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if placements['in_h1']:
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score += 5
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if placements['in_conclusion']:
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score += 5
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return score
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```
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---
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## 📁 Commands
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### **Analyze Keyword Density:**
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```bash
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python3 skills/seo-analyzers/scripts/thai_keyword_analyzer.py \
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--text "บทความเกี่ยวกับบริการ podcast hosting..." \
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--keyword "บริการ podcast" \
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--language th
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```
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### **Score Content Quality:**
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```bash
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python3 skills/seo-analyzers/scripts/content_quality_scorer.py \
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--file drafts/article.md \
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--keyword "podcast hosting" \
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--context "./website/context/"
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```
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### **Check Readability:**
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```bash
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python3 skills/seo-analyzers/scripts/thai_readability.py \
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--text "เนื้อหาบทความภาษาไทย..." \
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--language th
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```
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### **Clean AI Patterns:**
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```bash
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python3 skills/seo-analyzers/scripts/content_scrubber_thai.py \
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--file drafts/ai-generated.md \
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--output drafts/cleaned.md \
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--verbose
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```
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---
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## ⚙️ Environment Variables
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**Optional (in unified .env):**
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```bash
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# No API keys required for seo-analyzers
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# All processing is local with PyThaiNLP
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# Optional: For advanced NLP
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NLTK_DATA_PATH=/path/to/nltk_data
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```
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---
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## 📊 Output Examples
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### **Keyword Analysis Output:**
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```json
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{
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"word_count": 1847,
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"keyword": "บริการ podcast",
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"occurrences": 23,
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"density": 1.25,
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"status": "optimal",
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"critical_placements": {
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"in_first_100_words": true,
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"in_h1": true,
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"in_conclusion": true,
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"in_h2_count": 3
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},
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"keyword_stuffing_risk": "none",
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"recommendations": []
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}
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```
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### **Readability Output:**
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```json
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{
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"avg_sentence_length": 18.5,
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"grade_level": "ปานกลาง (ม.10-ม.12)",
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"formality": "ปกติ (Normal)",
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"score": 75,
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"details": {
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"sentence_count": 98,
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"paragraph_count": 24,
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"avg_paragraph_length": 4.1
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},
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"recommendations": [
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"ลดความยาวประโยคบ้าง (บางประโยคยาวเกินไป)",
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"รักษาระดับความเป็นกันเองนี้ไว้"
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]
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}
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```
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### **Quality Score Output:**
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```json
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{
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"overall_score": 82.5,
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"categories": {
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"keyword_optimization": 22.5,
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"readability": 20.0,
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"structure": 23.0,
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"brand_voice": 17.0
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},
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"status": "good",
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"publishing_readiness": "Ready with minor tweaks",
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"priority_fixes": [
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"ปรับ brand voice ให้เป็นกันเองมากขึ้น",
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"เพิ่ม internal links 2-3 แห่ง"
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],
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"recommendations": [
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"เพิ่มคำหลักใน H2 อีก 1-2 แห่ง",
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"ย่อหน้าบางตอนยาวเกินไป แบ่งออกเป็น 2 ย่อหน้า"
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]
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}
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```
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---
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## ✅ Quality Thresholds
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| Score Range | Status | Action |
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|-------------|--------|--------|
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| 90-100 | Excellent | Publish immediately |
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| 80-89 | Good | Minor tweaks, publishable |
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| 70-79 | Fair | Address priority fixes |
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| Below 70 | Needs Work | Significant improvements required |
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---
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## ⚠️ Important Notes
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1. **Thai Word Counting:** Uses PyThaiNLP for accurate counting (no spaces between Thai words)
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2. **Formality Detection:** Auto-detects from particles (ครับ/ค่ะ vs นะ/จ้ะ)
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3. **Keyword Density:** Thai target is 1.0-1.5% (lower than English 1.5-2.0%)
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4. **Readability:** Thai grade levels (ม.6-ม.12) instead of Flesch scores
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5. **AI Patterns:** Thai-specific patterns (overly formal, repetitive structures)
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---
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## 🔄 Integration with Other Skills
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- **seo-multi-channel:** Calls for quality scoring before output
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- **seo-context:** Loads brand voice for alignment scoring
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- **website-creator:** Validates content before publishing
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---
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**Use this skill when you need to analyze content quality, check keyword density, or clean AI patterns from Thai or English content.**
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