Analyze Skill

Extract themes, sentiment, and trends from aggregated customer feedback.

When to Use

• •After running /wicked-product:listen
• •User asks "what are customers saying about X"
• •Need to understand sentiment trends
• •Preparing customer insights for product decisions

Usage

# Analyze all recent feedback
/wicked-product:analyze

# Analyze specific theme
/wicked-product:analyze --theme "mobile experience"

# Analyze sentiment only
/wicked-product:analyze --sentiment negative

# Trend analysis
/wicked-product:analyze --trend "last-quarter"

# Segment analysis
/wicked-product:analyze --segment enterprise

Analysis Types

1. Sentiment Analysis

• •Classify: positive/negative/neutral/mixed
• •Score intensity: strong/moderate/mild
• •Indicators: See refs/sentiment-patterns.md

2. Theme Extraction

• •Cluster by keywords and co-occurrence
• •Categories: Product, Experience, Business
• •See refs/algorithms.md for clustering logic

3. Trend Detection

• •Compare time periods (week, month, quarter)
• •Categories: Emerging, Growing, Stable, Declining, Resolved
• •Threshold: >50% = Growing, >30% decline = Declining

4. Priority Scoring

Priority = (Frequency × Severity × Urgency) / Total_Feedback

See refs/algorithms.md for detailed scoring.

Analysis Process

1. •

   Load Feedback Data:

   bash

   # Count total items
   find ~/.something-wicked/voice/feedback/ -name "*.md" | wc -l
   
   # Load recent feedback
   find ~/.something-wicked/voice/feedback/ -name "*.md" -mtime -30
   

2. •

   Extract Themes:

   • •Keyword frequency analysis
   • •Tag co-occurrence
   • •Natural language clustering
3. •

   Score Sentiment:

   • •Classify each item (positive/negative/neutral/mixed)
   • •Score intensity (strong/moderate/mild)
   • •Calculate net sentiment
4. •

   Detect Trends:

   • •Group by time period (week, month, quarter)
   • •Compare frequencies
   • •Identify direction (up, down, stable)
5. •

   Segment Analysis:

   • •Group by customer segment (if available)
   • •Compare sentiment across segments
   • •Identify segment-specific themes

Output Format

## Analysis: {Topic or Timeframe}

### Sentiment Overview
- Net Sentiment: {+/-N} ({X}% positive, {Y}% negative)
- Strong Emotions: {count} highly positive, {count} highly negative
- Trend: {IMPROVING/DECLINING/STABLE} compared to previous period

### Top Themes (by priority)
1. **{Theme}** - Priority: {score}
   - Frequency: {count} mentions ({%} of total)
   - Sentiment: {positive/negative/mixed} ({intensity})
   - Trend: {GROWING/STABLE/DECLINING} ({+/-X}% vs. baseline)
   - Severity: {CRITICAL/HIGH/MEDIUM/LOW}
   - Key quote: "{representative example}"

{Top 5 themes}

### Emerging Patterns
- **{New Theme}**: Recently appeared, {count} mentions
- **{Growing Theme}**: {X}% increase from last period

### Segment Insights
- **{Segment}**: {sentiment + top theme}

### Recommendations
{1-3 actionable recommendations based on analysis}

Technical Implementation

See refs/algorithms.md for detailed analysis algorithms.

Integration

With wicked-crew (Auto Context)

# Triggered by product:requirements:started event
if event.type == "product:requirements:started":
    # Analyze recent feedback for feature context
    analysis = analyze(days=30, tags=["feature-request"])
    emit_signal("voice:analysis:ready", analysis)

With wicked-kanban (Link to Tasks)

# Tag themes with related task IDs
if has_plugin("wicked-kanban"):
    tasks = search_tasks(theme.keywords)
    theme.related_tasks = tasks

Storage

Analysis results stored at: ~/.something-wicked/voice/analysis/{theme}/{date}.md

Rules

• •Limit to top 5 themes (context efficiency)
• •Always include sample size (N=X)
• •Provide confidence levels for trends
• •Include representative quotes
• •Keep output under 800 words
• •Distinguish frequency from severity