Initial commit: The Ultimate Antigravity Skills Collection (58 Skills)

skills/product-manager-toolkit/SKILL.md

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+---
+name: product-manager-toolkit
+description: Comprehensive toolkit for product managers including RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, and go-to-market strategies. Use for feature prioritization, user research synthesis, requirement documentation, and product strategy development.
+---
+
+# Product Manager Toolkit
+
+Essential tools and frameworks for modern product management, from discovery to delivery.
+
+## Quick Start
+
+### For Feature Prioritization
+```bash
+python scripts/rice_prioritizer.py sample  # Create sample CSV
+python scripts/rice_prioritizer.py sample_features.csv --capacity 15
+```
+
+### For Interview Analysis
+```bash
+python scripts/customer_interview_analyzer.py interview_transcript.txt
+```
+
+### For PRD Creation
+1. Choose template from `references/prd_templates.md`
+2. Fill in sections based on discovery work
+3. Review with stakeholders
+4. Version control in your PM tool
+
+## Core Workflows
+
+### Feature Prioritization Process
+
+1. **Gather Feature Requests**
+   - Customer feedback
+   - Sales requests
+   - Technical debt
+   - Strategic initiatives
+
+2. **Score with RICE**
+   ```bash
+   # Create CSV with: name,reach,impact,confidence,effort
+   python scripts/rice_prioritizer.py features.csv
+   ```
+   - **Reach**: Users affected per quarter
+   - **Impact**: massive/high/medium/low/minimal
+   - **Confidence**: high/medium/low
+   - **Effort**: xl/l/m/s/xs (person-months)
+
+3. **Analyze Portfolio**
+   - Review quick wins vs big bets
+   - Check effort distribution
+   - Validate against strategy
+
+4. **Generate Roadmap**
+   - Quarterly capacity planning
+   - Dependency mapping
+   - Stakeholder alignment
+
+### Customer Discovery Process
+
+1. **Conduct Interviews**
+   - Use semi-structured format
+   - Focus on problems, not solutions
+   - Record with permission
+
+2. **Analyze Insights**
+   ```bash
+   python scripts/customer_interview_analyzer.py transcript.txt
+   ```
+   Extracts:
+   - Pain points with severity
+   - Feature requests with priority
+   - Jobs to be done
+   - Sentiment analysis
+   - Key themes and quotes
+
+3. **Synthesize Findings**
+   - Group similar pain points
+   - Identify patterns across interviews
+   - Map to opportunity areas
+
+4. **Validate Solutions**
+   - Create solution hypotheses
+   - Test with prototypes
+   - Measure actual vs expected behavior
+
+### PRD Development Process
+
+1. **Choose Template**
+   - **Standard PRD**: Complex features (6-8 weeks)
+   - **One-Page PRD**: Simple features (2-4 weeks)
+   - **Feature Brief**: Exploration phase (1 week)
+   - **Agile Epic**: Sprint-based delivery
+
+2. **Structure Content**
+   - Problem → Solution → Success Metrics
+   - Always include out-of-scope
+   - Clear acceptance criteria
+
+3. **Collaborate**
+   - Engineering for feasibility
+   - Design for experience
+   - Sales for market validation
+   - Support for operational impact
+
+## Key Scripts
+
+### rice_prioritizer.py
+Advanced RICE framework implementation with portfolio analysis.
+
+**Features**:
+- RICE score calculation
+- Portfolio balance analysis (quick wins vs big bets)
+- Quarterly roadmap generation
+- Team capacity planning
+- Multiple output formats (text/json/csv)
+
+**Usage Examples**:
+```bash
+# Basic prioritization
+python scripts/rice_prioritizer.py features.csv
+
+# With custom team capacity (person-months per quarter)
+python scripts/rice_prioritizer.py features.csv --capacity 20
+
+# Output as JSON for integration
+python scripts/rice_prioritizer.py features.csv --output json
+```
+
+### customer_interview_analyzer.py
+NLP-based interview analysis for extracting actionable insights.
+
+**Capabilities**:
+- Pain point extraction with severity assessment
+- Feature request identification and classification
+- Jobs-to-be-done pattern recognition
+- Sentiment analysis
+- Theme extraction
+- Competitor mentions
+- Key quotes identification
+
+**Usage Examples**:
+```bash
+# Analyze single interview
+python scripts/customer_interview_analyzer.py interview.txt
+
+# Output as JSON for aggregation
+python scripts/customer_interview_analyzer.py interview.txt json
+```
+
+## Reference Documents
+
+### prd_templates.md
+Multiple PRD formats for different contexts:
+
+1. **Standard PRD Template**
+   - Comprehensive 11-section format
+   - Best for major features
+   - Includes technical specs
+
+2. **One-Page PRD**
+   - Concise format for quick alignment
+   - Focus on problem/solution/metrics
+   - Good for smaller features
+
+3. **Agile Epic Template**
+   - Sprint-based delivery
+   - User story mapping
+   - Acceptance criteria focus
+
+4. **Feature Brief**
+   - Lightweight exploration
+   - Hypothesis-driven
+   - Pre-PRD phase
+
+## Prioritization Frameworks
+
+### RICE Framework
+```
+Score = (Reach × Impact × Confidence) / Effort
+
+Reach: # of users/quarter
+Impact: 
+  - Massive = 3x
+  - High = 2x
+  - Medium = 1x
+  - Low = 0.5x
+  - Minimal = 0.25x
+Confidence:
+  - High = 100%
+  - Medium = 80%
+  - Low = 50%
+Effort: Person-months
+```
+
+### Value vs Effort Matrix
+```
+         Low Effort    High Effort
+         
+High     QUICK WINS    BIG BETS
+Value    [Prioritize]   [Strategic]
+         
+Low      FILL-INS      TIME SINKS
+Value    [Maybe]       [Avoid]
+```
+
+### MoSCoW Method
+- **Must Have**: Critical for launch
+- **Should Have**: Important but not critical
+- **Could Have**: Nice to have
+- **Won't Have**: Out of scope
+
+## Discovery Frameworks
+
+### Customer Interview Guide
+```
+1. Context Questions (5 min)
+   - Role and responsibilities
+   - Current workflow
+   - Tools used
+
+2. Problem Exploration (15 min)
+   - Pain points
+   - Frequency and impact
+   - Current workarounds
+
+3. Solution Validation (10 min)
+   - Reaction to concepts
+   - Value perception
+   - Willingness to pay
+
+4. Wrap-up (5 min)
+   - Other thoughts
+   - Referrals
+   - Follow-up permission
+```
+
+### Hypothesis Template
+```
+We believe that [building this feature]
+For [these users]
+Will [achieve this outcome]
+We'll know we're right when [metric]
+```
+
+### Opportunity Solution Tree
+```
+Outcome
+├── Opportunity 1
+│   ├── Solution A
+│   └── Solution B
+└── Opportunity 2
+    ├── Solution C
+    └── Solution D
+```
+
+## Metrics & Analytics
+
+### North Star Metric Framework
+1. **Identify Core Value**: What's the #1 value to users?
+2. **Make it Measurable**: Quantifiable and trackable
+3. **Ensure It's Actionable**: Teams can influence it
+4. **Check Leading Indicator**: Predicts business success
+
+### Funnel Analysis Template
+```
+Acquisition → Activation → Retention → Revenue → Referral
+
+Key Metrics:
+- Conversion rate at each step
+- Drop-off points
+- Time between steps
+- Cohort variations
+```
+
+### Feature Success Metrics
+- **Adoption**: % of users using feature
+- **Frequency**: Usage per user per time period
+- **Depth**: % of feature capability used
+- **Retention**: Continued usage over time
+- **Satisfaction**: NPS/CSAT for feature
+
+## Best Practices
+
+### Writing Great PRDs
+1. Start with the problem, not solution
+2. Include clear success metrics upfront
+3. Explicitly state what's out of scope
+4. Use visuals (wireframes, flows)
+5. Keep technical details in appendix
+6. Version control changes
+
+### Effective Prioritization
+1. Mix quick wins with strategic bets
+2. Consider opportunity cost
+3. Account for dependencies
+4. Buffer for unexpected work (20%)
+5. Revisit quarterly
+6. Communicate decisions clearly
+
+### Customer Discovery Tips
+1. Ask "why" 5 times
+2. Focus on past behavior, not future intentions
+3. Avoid leading questions
+4. Interview in their environment
+5. Look for emotional reactions
+6. Validate with data
+
+### Stakeholder Management
+1. Identify RACI for decisions
+2. Regular async updates
+3. Demo over documentation
+4. Address concerns early
+5. Celebrate wins publicly
+6. Learn from failures openly
+
+## Common Pitfalls to Avoid
+
+1. **Solution-First Thinking**: Jumping to features before understanding problems
+2. **Analysis Paralysis**: Over-researching without shipping
+3. **Feature Factory**: Shipping features without measuring impact
+4. **Ignoring Technical Debt**: Not allocating time for platform health
+5. **Stakeholder Surprise**: Not communicating early and often
+6. **Metric Theater**: Optimizing vanity metrics over real value
+
+## Integration Points
+
+This toolkit integrates with:
+- **Analytics**: Amplitude, Mixpanel, Google Analytics
+- **Roadmapping**: ProductBoard, Aha!, Roadmunk
+- **Design**: Figma, Sketch, Miro
+- **Development**: Jira, Linear, GitHub
+- **Research**: Dovetail, UserVoice, Pendo
+- **Communication**: Slack, Notion, Confluence
+
+## Quick Commands Cheat Sheet
+
+```bash
+# Prioritization
+python scripts/rice_prioritizer.py features.csv --capacity 15
+
+# Interview Analysis
+python scripts/customer_interview_analyzer.py interview.txt
+
+# Create sample data
+python scripts/rice_prioritizer.py sample
+
+# JSON outputs for integration
+python scripts/rice_prioritizer.py features.csv --output json
+python scripts/customer_interview_analyzer.py interview.txt json
+```

skills/product-manager-toolkit/references/prd_templates.md

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+# Product Requirements Document (PRD) Templates
+
+## Standard PRD Template
+
+### 1. Executive Summary
+**Purpose**: One-page overview for executives and stakeholders
+
+#### Components:
+- **Problem Statement** (2-3 sentences)
+- **Proposed Solution** (2-3 sentences)
+- **Business Impact** (3 bullet points)
+- **Timeline** (High-level milestones)
+- **Resources Required** (Team size and budget)
+- **Success Metrics** (3-5 KPIs)
+
+### 2. Problem Definition
+
+#### 2.1 Customer Problem
+- **Who**: Target user persona(s)
+- **What**: Specific problem or need
+- **When**: Context and frequency
+- **Where**: Environment and touchpoints
+- **Why**: Root cause analysis
+- **Impact**: Cost of not solving
+
+#### 2.2 Market Opportunity
+- **Market Size**: TAM, SAM, SOM
+- **Growth Rate**: Annual growth percentage
+- **Competition**: Current solutions and gaps
+- **Timing**: Why now?
+
+#### 2.3 Business Case
+- **Revenue Potential**: Projected impact
+- **Cost Savings**: Efficiency gains
+- **Strategic Value**: Alignment with company goals
+- **Risk Assessment**: What if we don't do this?
+
+### 3. Solution Overview
+
+#### 3.1 Proposed Solution
+- **High-Level Description**: What we're building
+- **Key Capabilities**: Core functionality
+- **User Journey**: End-to-end flow
+- **Differentiation**: Unique value proposition
+
+#### 3.2 In Scope
+- Feature 1: Description and priority
+- Feature 2: Description and priority
+- Feature 3: Description and priority
+
+#### 3.3 Out of Scope
+- Explicitly what we're NOT doing
+- Future considerations
+- Dependencies on other teams
+
+#### 3.4 MVP Definition
+- **Core Features**: Minimum viable feature set
+- **Success Criteria**: Definition of "working"
+- **Timeline**: MVP delivery date
+- **Learning Goals**: What we want to validate
+
+### 4. User Stories & Requirements
+
+#### 4.1 User Stories
+```
+As a [persona]
+I want to [action]
+So that [outcome/benefit]
+
+Acceptance Criteria:
+- [ ] Criterion 1
+- [ ] Criterion 2
+- [ ] Criterion 3
+```
+
+#### 4.2 Functional Requirements
+| ID | Requirement | Priority | Notes |
+|----|------------|----------|-------|
+| FR1 | User can... | P0 | Critical for MVP |
+| FR2 | System should... | P1 | Important |
+| FR3 | Feature must... | P2 | Nice to have |
+
+#### 4.3 Non-Functional Requirements
+- **Performance**: Response times, throughput
+- **Scalability**: User/data growth targets
+- **Security**: Authentication, authorization, data protection
+- **Reliability**: Uptime targets, error rates
+- **Usability**: Accessibility standards, device support
+- **Compliance**: Regulatory requirements
+
+### 5. Design & User Experience
+
+#### 5.1 Design Principles
+- Principle 1: Description
+- Principle 2: Description
+- Principle 3: Description
+
+#### 5.2 Wireframes/Mockups
+- Link to Figma/Sketch files
+- Key screens and flows
+- Interaction patterns
+
+#### 5.3 Information Architecture
+- Navigation structure
+- Data organization
+- Content hierarchy
+
+### 6. Technical Specifications
+
+#### 6.1 Architecture Overview
+- System architecture diagram
+- Technology stack
+- Integration points
+- Data flow
+
+#### 6.2 API Design
+- Endpoints and methods
+- Request/response formats
+- Authentication approach
+- Rate limiting
+
+#### 6.3 Database Design
+- Data model
+- Key entities and relationships
+- Migration strategy
+
+#### 6.4 Security Considerations
+- Authentication method
+- Authorization model
+- Data encryption
+- PII handling
+
+### 7. Go-to-Market Strategy
+
+#### 7.1 Launch Plan
+- **Soft Launch**: Beta users, timeline
+- **Full Launch**: All users, timeline
+- **Marketing**: Campaigns and channels
+- **Support**: Documentation and training
+
+#### 7.2 Pricing Strategy
+- Pricing model
+- Competitive analysis
+- Value proposition
+
+#### 7.3 Success Metrics
+| Metric | Target | Measurement Method |
+|--------|--------|-------------------|
+| Adoption Rate | X% | Daily Active Users |
+| User Satisfaction | X/10 | NPS Score |
+| Revenue Impact | $X | Monthly Recurring Revenue |
+| Performance | <Xms | P95 Response Time |
+
+### 8. Risks & Mitigations
+
+| Risk | Probability | Impact | Mitigation Strategy |
+|------|------------|--------|-------------------|
+| Technical debt | Medium | High | Allocate 20% for refactoring |
+| User adoption | Low | High | Beta program with feedback loops |
+| Scope creep | High | Medium | Weekly stakeholder reviews |
+
+### 9. Timeline & Milestones
+
+| Milestone | Date | Deliverables | Success Criteria |
+|-----------|------|--------------|-----------------|
+| Design Complete | Week 2 | Mockups, IA | Stakeholder approval |
+| MVP Development | Week 6 | Core features | All P0s complete |
+| Beta Launch | Week 8 | Limited release | 100 beta users |
+| Full Launch | Week 12 | General availability | <1% error rate |
+
+### 10. Team & Resources
+
+#### 10.1 Team Structure
+- **Product Manager**: [Name]
+- **Engineering Lead**: [Name]
+- **Design Lead**: [Name]
+- **Engineers**: X FTEs
+- **QA**: X FTEs
+
+#### 10.2 Budget
+- Development: $X
+- Infrastructure: $X
+- Marketing: $X
+- Total: $X
+
+### 11. Appendix
+- User Research Data
+- Competitive Analysis
+- Technical Diagrams
+- Legal/Compliance Docs
+
+---
+
+## Agile Epic Template
+
+### Epic: [Epic Name]
+
+#### Overview
+**Epic ID**: EPIC-XXX
+**Theme**: [Product Theme]
+**Quarter**: QX 20XX
+**Status**: Discovery | In Progress | Complete
+
+#### Problem Statement
+[2-3 sentences describing the problem]
+
+#### Goals & Objectives
+1. Objective 1
+2. Objective 2
+3. Objective 3
+
+#### Success Metrics
+- Metric 1: Target
+- Metric 2: Target
+- Metric 3: Target
+
+#### User Stories
+| Story ID | Title | Priority | Points | Status |
+|----------|-------|----------|--------|--------|
+| US-001 | As a... | P0 | 5 | To Do |
+| US-002 | As a... | P1 | 3 | To Do |
+
+#### Dependencies
+- Dependency 1: Team/System
+- Dependency 2: Team/System
+
+#### Acceptance Criteria
+- [ ] All P0 stories complete
+- [ ] Performance targets met
+- [ ] Security review passed
+- [ ] Documentation updated
+
+---
+
+## One-Page PRD Template
+
+### [Feature Name] - One-Page PRD
+
+**Date**: [Date]
+**Author**: [PM Name]
+**Status**: Draft | In Review | Approved
+
+#### Problem
+*What problem are we solving? For whom?*
+[2-3 sentences]
+
+#### Solution
+*What are we building?*
+[2-3 sentences]
+
+#### Why Now?
+*What's driving urgency?*
+- Reason 1
+- Reason 2
+- Reason 3
+
+#### Success Metrics
+| Metric | Current | Target |
+|--------|---------|--------|
+| KPI 1 | X | Y |
+| KPI 2 | X | Y |
+
+#### Scope
+**In**: Feature 1, Feature 2, Feature 3
+**Out**: Feature A, Feature B
+
+#### User Flow
+```
+Step 1 → Step 2 → Step 3 → Success!
+```
+
+#### Risks
+1. Risk 1 → Mitigation
+2. Risk 2 → Mitigation
+
+#### Timeline
+- Design: Week 1-2
+- Development: Week 3-6
+- Testing: Week 7
+- Launch: Week 8
+
+#### Resources
+- Engineering: X developers
+- Design: X designer
+- QA: X tester
+
+#### Open Questions
+1. Question 1?
+2. Question 2?
+
+---
+
+## Feature Brief Template (Lightweight)
+
+### Feature: [Name]
+
+#### Context
+*Why are we considering this?*
+
+#### Hypothesis
+*We believe that [building this feature]
+For [these users]
+Will [achieve this outcome]
+We'll know we're right when [we see this metric]*
+
+#### Proposed Solution
+*High-level approach*
+
+#### Effort Estimate
+- **Size**: XS | S | M | L | XL
+- **Confidence**: High | Medium | Low
+
+#### Next Steps
+1. [ ] User research
+2. [ ] Design exploration
+3. [ ] Technical spike
+4. [ ] Stakeholder review

skills/product-manager-toolkit/scripts/customer_interview_analyzer.py

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+#!/usr/bin/env python3
+"""
+Customer Interview Analyzer
+Extracts insights, patterns, and opportunities from user interviews
+"""
+
+import re
+from typing import Dict, List, Tuple, Set
+from collections import Counter, defaultdict
+import json
+
+class InterviewAnalyzer:
+    """Analyze customer interviews for insights and patterns"""
+    
+    def __init__(self):
+        # Pain point indicators
+        self.pain_indicators = [
+            'frustrat', 'annoy', 'difficult', 'hard', 'confus', 'slow',
+            'problem', 'issue', 'struggle', 'challeng', 'pain', 'waste',
+            'manual', 'repetitive', 'tedious', 'boring', 'time-consuming',
+            'complicated', 'complex', 'unclear', 'wish', 'need', 'want'
+        ]
+        
+        # Positive indicators
+        self.delight_indicators = [
+            'love', 'great', 'awesome', 'amazing', 'perfect', 'easy',
+            'simple', 'quick', 'fast', 'helpful', 'useful', 'valuable',
+            'save', 'efficient', 'convenient', 'intuitive', 'clear'
+        ]
+        
+        # Feature request indicators
+        self.request_indicators = [
+            'would be nice', 'wish', 'hope', 'want', 'need', 'should',
+            'could', 'would love', 'if only', 'it would help', 'suggest',
+            'recommend', 'idea', 'what if', 'have you considered'
+        ]
+        
+        # Jobs to be done patterns
+        self.jtbd_patterns = [
+            r'when i\s+(.+?),\s+i want to\s+(.+?)\s+so that\s+(.+)',
+            r'i need to\s+(.+?)\s+because\s+(.+)',
+            r'my goal is to\s+(.+)',
+            r'i\'m trying to\s+(.+)',
+            r'i use \w+ to\s+(.+)',
+            r'helps me\s+(.+)',
+        ]
+    
+    def analyze_interview(self, text: str) -> Dict:
+        """Analyze a single interview transcript"""
+        text_lower = text.lower()
+        sentences = self._split_sentences(text)
+        
+        analysis = {
+            'pain_points': self._extract_pain_points(sentences),
+            'delights': self._extract_delights(sentences),
+            'feature_requests': self._extract_requests(sentences),
+            'jobs_to_be_done': self._extract_jtbd(text_lower),
+            'sentiment_score': self._calculate_sentiment(text_lower),
+            'key_themes': self._extract_themes(text_lower),
+            'quotes': self._extract_key_quotes(sentences),
+            'metrics_mentioned': self._extract_metrics(text),
+            'competitors_mentioned': self._extract_competitors(text)
+        }
+        
+        return analysis
+    
+    def _split_sentences(self, text: str) -> List[str]:
+        """Split text into sentences"""
+        # Simple sentence splitting
+        sentences = re.split(r'[.!?]+', text)
+        return [s.strip() for s in sentences if s.strip()]
+    
+    def _extract_pain_points(self, sentences: List[str]) -> List[Dict]:
+        """Extract pain points from sentences"""
+        pain_points = []
+        
+        for sentence in sentences:
+            sentence_lower = sentence.lower()
+            for indicator in self.pain_indicators:
+                if indicator in sentence_lower:
+                    # Extract context around the pain point
+                    pain_points.append({
+                        'quote': sentence,
+                        'indicator': indicator,
+                        'severity': self._assess_severity(sentence_lower)
+                    })
+                    break
+        
+        return pain_points[:10]  # Return top 10
+    
+    def _extract_delights(self, sentences: List[str]) -> List[Dict]:
+        """Extract positive feedback"""
+        delights = []
+        
+        for sentence in sentences:
+            sentence_lower = sentence.lower()
+            for indicator in self.delight_indicators:
+                if indicator in sentence_lower:
+                    delights.append({
+                        'quote': sentence,
+                        'indicator': indicator,
+                        'strength': self._assess_strength(sentence_lower)
+                    })
+                    break
+        
+        return delights[:10]
+    
+    def _extract_requests(self, sentences: List[str]) -> List[Dict]:
+        """Extract feature requests and suggestions"""
+        requests = []
+        
+        for sentence in sentences:
+            sentence_lower = sentence.lower()
+            for indicator in self.request_indicators:
+                if indicator in sentence_lower:
+                    requests.append({
+                        'quote': sentence,
+                        'type': self._classify_request(sentence_lower),
+                        'priority': self._assess_request_priority(sentence_lower)
+                    })
+                    break
+        
+        return requests[:10]
+    
+    def _extract_jtbd(self, text: str) -> List[Dict]:
+        """Extract Jobs to Be Done patterns"""
+        jobs = []
+        
+        for pattern in self.jtbd_patterns:
+            matches = re.findall(pattern, text, re.IGNORECASE)
+            for match in matches:
+                if isinstance(match, tuple):
+                    job = ' → '.join(match)
+                else:
+                    job = match
+                
+                jobs.append({
+                    'job': job,
+                    'pattern': pattern.pattern if hasattr(pattern, 'pattern') else pattern
+                })
+        
+        return jobs[:5]
+    
+    def _calculate_sentiment(self, text: str) -> Dict:
+        """Calculate overall sentiment of the interview"""
+        positive_count = sum(1 for ind in self.delight_indicators if ind in text)
+        negative_count = sum(1 for ind in self.pain_indicators if ind in text)
+        
+        total = positive_count + negative_count
+        if total == 0:
+            sentiment_score = 0
+        else:
+            sentiment_score = (positive_count - negative_count) / total
+        
+        if sentiment_score > 0.3:
+            sentiment_label = 'positive'
+        elif sentiment_score < -0.3:
+            sentiment_label = 'negative'
+        else:
+            sentiment_label = 'neutral'
+        
+        return {
+            'score': round(sentiment_score, 2),
+            'label': sentiment_label,
+            'positive_signals': positive_count,
+            'negative_signals': negative_count
+        }
+    
+    def _extract_themes(self, text: str) -> List[str]:
+        """Extract key themes using word frequency"""
+        # Remove common words
+        stop_words = {'the', 'a', 'an', 'and', 'or', 'but', 'in', 'on', 'at',
+                     'to', 'for', 'of', 'with', 'by', 'from', 'as', 'is',
+                     'was', 'are', 'were', 'been', 'be', 'have', 'has',
+                     'had', 'do', 'does', 'did', 'will', 'would', 'could',
+                     'should', 'may', 'might', 'must', 'can', 'shall',
+                     'it', 'i', 'you', 'we', 'they', 'them', 'their'}
+        
+        # Extract meaningful words
+        words = re.findall(r'\b[a-z]{4,}\b', text)
+        meaningful_words = [w for w in words if w not in stop_words]
+        
+        # Count frequency
+        word_freq = Counter(meaningful_words)
+        
+        # Extract themes (top frequent meaningful words)
+        themes = [word for word, count in word_freq.most_common(10) if count >= 3]
+        
+        return themes
+    
+    def _extract_key_quotes(self, sentences: List[str]) -> List[str]:
+        """Extract the most insightful quotes"""
+        scored_sentences = []
+        
+        for sentence in sentences:
+            if len(sentence) < 20 or len(sentence) > 200:
+                continue
+            
+            score = 0
+            sentence_lower = sentence.lower()
+            
+            # Score based on insight indicators
+            if any(ind in sentence_lower for ind in self.pain_indicators):
+                score += 2
+            if any(ind in sentence_lower for ind in self.request_indicators):
+                score += 2
+            if 'because' in sentence_lower:
+                score += 1
+            if 'but' in sentence_lower:
+                score += 1
+            if '?' in sentence:
+                score += 1
+            
+            if score > 0:
+                scored_sentences.append((score, sentence))
+        
+        # Sort by score and return top quotes
+        scored_sentences.sort(reverse=True)
+        return [s[1] for s in scored_sentences[:5]]
+    
+    def _extract_metrics(self, text: str) -> List[str]:
+        """Extract any metrics or numbers mentioned"""
+        metrics = []
+        
+        # Find percentages
+        percentages = re.findall(r'\d+%', text)
+        metrics.extend(percentages)
+        
+        # Find time metrics
+        time_metrics = re.findall(r'\d+\s*(?:hours?|minutes?|days?|weeks?|months?)', text, re.IGNORECASE)
+        metrics.extend(time_metrics)
+        
+        # Find money metrics
+        money_metrics = re.findall(r'\$[\d,]+', text)
+        metrics.extend(money_metrics)
+        
+        # Find general numbers with context
+        number_contexts = re.findall(r'(\d+)\s+(\w+)', text)
+        for num, context in number_contexts:
+            if context.lower() not in ['the', 'a', 'an', 'and', 'or', 'of']:
+                metrics.append(f"{num} {context}")
+        
+        return list(set(metrics))[:10]
+    
+    def _extract_competitors(self, text: str) -> List[str]:
+        """Extract competitor mentions"""
+        # Common competitor indicators
+        competitor_patterns = [
+            r'(?:use|used|using|tried|trying|switch from|switched from|instead of)\s+(\w+)',
+            r'(\w+)\s+(?:is better|works better|is easier)',
+            r'compared to\s+(\w+)',
+            r'like\s+(\w+)',
+            r'similar to\s+(\w+)',
+        ]
+        
+        competitors = set()
+        for pattern in competitor_patterns:
+            matches = re.findall(pattern, text, re.IGNORECASE)
+            competitors.update(matches)
+        
+        # Filter out common words
+        common_words = {'this', 'that', 'it', 'them', 'other', 'another', 'something'}
+        competitors = [c for c in competitors if c.lower() not in common_words and len(c) > 2]
+        
+        return list(competitors)[:5]
+    
+    def _assess_severity(self, text: str) -> str:
+        """Assess severity of pain point"""
+        if any(word in text for word in ['very', 'extremely', 'really', 'totally', 'completely']):
+            return 'high'
+        elif any(word in text for word in ['somewhat', 'bit', 'little', 'slightly']):
+            return 'low'
+        return 'medium'
+    
+    def _assess_strength(self, text: str) -> str:
+        """Assess strength of positive feedback"""
+        if any(word in text for word in ['absolutely', 'definitely', 'really', 'very']):
+            return 'strong'
+        return 'moderate'
+    
+    def _classify_request(self, text: str) -> str:
+        """Classify the type of request"""
+        if any(word in text for word in ['ui', 'design', 'look', 'color', 'layout']):
+            return 'ui_improvement'
+        elif any(word in text for word in ['feature', 'add', 'new', 'build']):
+            return 'new_feature'
+        elif any(word in text for word in ['fix', 'bug', 'broken', 'work']):
+            return 'bug_fix'
+        elif any(word in text for word in ['faster', 'slow', 'performance', 'speed']):
+            return 'performance'
+        return 'general'
+    
+    def _assess_request_priority(self, text: str) -> str:
+        """Assess priority of request"""
+        if any(word in text for word in ['critical', 'urgent', 'asap', 'immediately', 'blocking']):
+            return 'critical'
+        elif any(word in text for word in ['need', 'important', 'should', 'must']):
+            return 'high'
+        elif any(word in text for word in ['nice', 'would', 'could', 'maybe']):
+            return 'low'
+        return 'medium'
+
+def aggregate_interviews(interviews: List[Dict]) -> Dict:
+    """Aggregate insights from multiple interviews"""
+    aggregated = {
+        'total_interviews': len(interviews),
+        'common_pain_points': defaultdict(list),
+        'common_requests': defaultdict(list),
+        'jobs_to_be_done': [],
+        'overall_sentiment': {
+            'positive': 0,
+            'negative': 0,
+            'neutral': 0
+        },
+        'top_themes': Counter(),
+        'metrics_summary': set(),
+        'competitors_mentioned': Counter()
+    }
+    
+    for interview in interviews:
+        # Aggregate pain points
+        for pain in interview.get('pain_points', []):
+            indicator = pain.get('indicator', 'unknown')
+            aggregated['common_pain_points'][indicator].append(pain['quote'])
+        
+        # Aggregate requests
+        for request in interview.get('feature_requests', []):
+            req_type = request.get('type', 'general')
+            aggregated['common_requests'][req_type].append(request['quote'])
+        
+        # Aggregate JTBD
+        aggregated['jobs_to_be_done'].extend(interview.get('jobs_to_be_done', []))
+        
+        # Aggregate sentiment
+        sentiment = interview.get('sentiment_score', {}).get('label', 'neutral')
+        aggregated['overall_sentiment'][sentiment] += 1
+        
+        # Aggregate themes
+        for theme in interview.get('key_themes', []):
+            aggregated['top_themes'][theme] += 1
+        
+        # Aggregate metrics
+        aggregated['metrics_summary'].update(interview.get('metrics_mentioned', []))
+        
+        # Aggregate competitors
+        for competitor in interview.get('competitors_mentioned', []):
+            aggregated['competitors_mentioned'][competitor] += 1
+    
+    # Process aggregated data
+    aggregated['common_pain_points'] = dict(aggregated['common_pain_points'])
+    aggregated['common_requests'] = dict(aggregated['common_requests'])
+    aggregated['top_themes'] = dict(aggregated['top_themes'].most_common(10))
+    aggregated['metrics_summary'] = list(aggregated['metrics_summary'])
+    aggregated['competitors_mentioned'] = dict(aggregated['competitors_mentioned'])
+    
+    return aggregated
+
+def format_single_interview(analysis: Dict) -> str:
+    """Format single interview analysis"""
+    output = ["=" * 60]
+    output.append("CUSTOMER INTERVIEW ANALYSIS")
+    output.append("=" * 60)
+    
+    # Sentiment
+    sentiment = analysis['sentiment_score']
+    output.append(f"\n📊 Overall Sentiment: {sentiment['label'].upper()}")
+    output.append(f"   Score: {sentiment['score']}")
+    output.append(f"   Positive signals: {sentiment['positive_signals']}")
+    output.append(f"   Negative signals: {sentiment['negative_signals']}")
+    
+    # Pain Points
+    if analysis['pain_points']:
+        output.append("\n🔥 Pain Points Identified:")
+        for i, pain in enumerate(analysis['pain_points'][:5], 1):
+            output.append(f"\n{i}. [{pain['severity'].upper()}] {pain['quote'][:100]}...")
+    
+    # Feature Requests
+    if analysis['feature_requests']:
+        output.append("\n💡 Feature Requests:")
+        for i, req in enumerate(analysis['feature_requests'][:5], 1):
+            output.append(f"\n{i}. [{req['type']}] Priority: {req['priority']}")
+            output.append(f"   \"{req['quote'][:100]}...\"")
+    
+    # Jobs to Be Done
+    if analysis['jobs_to_be_done']:
+        output.append("\n🎯 Jobs to Be Done:")
+        for i, job in enumerate(analysis['jobs_to_be_done'], 1):
+            output.append(f"{i}. {job['job']}")
+    
+    # Key Themes
+    if analysis['key_themes']:
+        output.append("\n🏷️ Key Themes:")
+        output.append(", ".join(analysis['key_themes']))
+    
+    # Key Quotes
+    if analysis['quotes']:
+        output.append("\n💬 Key Quotes:")
+        for i, quote in enumerate(analysis['quotes'][:3], 1):
+            output.append(f'{i}. "{quote}"')
+    
+    # Metrics
+    if analysis['metrics_mentioned']:
+        output.append("\n📈 Metrics Mentioned:")
+        output.append(", ".join(analysis['metrics_mentioned']))
+    
+    # Competitors
+    if analysis['competitors_mentioned']:
+        output.append("\n🏢 Competitors Mentioned:")
+        output.append(", ".join(analysis['competitors_mentioned']))
+    
+    return "\n".join(output)
+
+def main():
+    import sys
+    
+    if len(sys.argv) < 2:
+        print("Usage: python customer_interview_analyzer.py <interview_file.txt>")
+        print("\nThis tool analyzes customer interview transcripts to extract:")
+        print("  - Pain points and frustrations")
+        print("  - Feature requests and suggestions")
+        print("  - Jobs to be done")
+        print("  - Sentiment analysis")
+        print("  - Key themes and quotes")
+        sys.exit(1)
+    
+    # Read interview transcript
+    with open(sys.argv[1], 'r') as f:
+        interview_text = f.read()
+    
+    # Analyze
+    analyzer = InterviewAnalyzer()
+    analysis = analyzer.analyze_interview(interview_text)
+    
+    # Output
+    if len(sys.argv) > 2 and sys.argv[2] == 'json':
+        print(json.dumps(analysis, indent=2))
+    else:
+        print(format_single_interview(analysis))
+
+if __name__ == "__main__":
+    main()

skills/product-manager-toolkit/scripts/rice_prioritizer.py

@@ -0,0 +1,296 @@
+#!/usr/bin/env python3
+"""
+RICE Prioritization Framework
+Calculates RICE scores for feature prioritization
+RICE = (Reach x Impact x Confidence) / Effort
+"""
+
+import json
+import csv
+from typing import List, Dict, Tuple
+import argparse
+
+class RICECalculator:
+    """Calculate RICE scores for feature prioritization"""
+    
+    def __init__(self):
+        self.impact_map = {
+            'massive': 3.0,
+            'high': 2.0,
+            'medium': 1.0,
+            'low': 0.5,
+            'minimal': 0.25
+        }
+        
+        self.confidence_map = {
+            'high': 100,
+            'medium': 80,
+            'low': 50
+        }
+        
+        self.effort_map = {
+            'xl': 13,
+            'l': 8,
+            'm': 5,
+            's': 3,
+            'xs': 1
+        }
+    
+    def calculate_rice(self, reach: int, impact: str, confidence: str, effort: str) -> float:
+        """
+        Calculate RICE score
+        
+        Args:
+            reach: Number of users/customers affected per quarter
+            impact: massive/high/medium/low/minimal
+            confidence: high/medium/low (percentage)
+            effort: xl/l/m/s/xs (person-months)
+        """
+        impact_score = self.impact_map.get(impact.lower(), 1.0)
+        confidence_score = self.confidence_map.get(confidence.lower(), 50) / 100
+        effort_score = self.effort_map.get(effort.lower(), 5)
+        
+        if effort_score == 0:
+            return 0
+        
+        rice_score = (reach * impact_score * confidence_score) / effort_score
+        return round(rice_score, 2)
+    
+    def prioritize_features(self, features: List[Dict]) -> List[Dict]:
+        """
+        Calculate RICE scores and rank features
+        
+        Args:
+            features: List of feature dictionaries with RICE components
+        """
+        for feature in features:
+            feature['rice_score'] = self.calculate_rice(
+                feature.get('reach', 0),
+                feature.get('impact', 'medium'),
+                feature.get('confidence', 'medium'),
+                feature.get('effort', 'm')
+            )
+        
+        # Sort by RICE score descending
+        return sorted(features, key=lambda x: x['rice_score'], reverse=True)
+    
+    def analyze_portfolio(self, features: List[Dict]) -> Dict:
+        """
+        Analyze the feature portfolio for balance and insights
+        """
+        if not features:
+            return {}
+        
+        total_effort = sum(
+            self.effort_map.get(f.get('effort', 'm').lower(), 5) 
+            for f in features
+        )
+        
+        total_reach = sum(f.get('reach', 0) for f in features)
+        
+        effort_distribution = {}
+        impact_distribution = {}
+        
+        for feature in features:
+            effort = feature.get('effort', 'm').lower()
+            impact = feature.get('impact', 'medium').lower()
+            
+            effort_distribution[effort] = effort_distribution.get(effort, 0) + 1
+            impact_distribution[impact] = impact_distribution.get(impact, 0) + 1
+        
+        # Calculate quick wins (high impact, low effort)
+        quick_wins = [
+            f for f in features 
+            if f.get('impact', '').lower() in ['massive', 'high'] 
+            and f.get('effort', '').lower() in ['xs', 's']
+        ]
+        
+        # Calculate big bets (high impact, high effort)
+        big_bets = [
+            f for f in features 
+            if f.get('impact', '').lower() in ['massive', 'high'] 
+            and f.get('effort', '').lower() in ['l', 'xl']
+        ]
+        
+        return {
+            'total_features': len(features),
+            'total_effort_months': total_effort,
+            'total_reach': total_reach,
+            'average_rice': round(sum(f['rice_score'] for f in features) / len(features), 2),
+            'effort_distribution': effort_distribution,
+            'impact_distribution': impact_distribution,
+            'quick_wins': len(quick_wins),
+            'big_bets': len(big_bets),
+            'quick_wins_list': quick_wins[:3],  # Top 3 quick wins
+            'big_bets_list': big_bets[:3]  # Top 3 big bets
+        }
+    
+    def generate_roadmap(self, features: List[Dict], team_capacity: int = 10) -> List[Dict]:
+        """
+        Generate a quarterly roadmap based on team capacity
+        
+        Args:
+            features: Prioritized feature list
+            team_capacity: Person-months available per quarter
+        """
+        quarters = []
+        current_quarter = {
+            'quarter': 1,
+            'features': [],
+            'capacity_used': 0,
+            'capacity_available': team_capacity
+        }
+        
+        for feature in features:
+            effort = self.effort_map.get(feature.get('effort', 'm').lower(), 5)
+            
+            if current_quarter['capacity_used'] + effort <= team_capacity:
+                current_quarter['features'].append(feature)
+                current_quarter['capacity_used'] += effort
+            else:
+                # Move to next quarter
+                current_quarter['capacity_available'] = team_capacity - current_quarter['capacity_used']
+                quarters.append(current_quarter)
+                
+                current_quarter = {
+                    'quarter': len(quarters) + 1,
+                    'features': [feature],
+                    'capacity_used': effort,
+                    'capacity_available': team_capacity - effort
+                }
+        
+        if current_quarter['features']:
+            current_quarter['capacity_available'] = team_capacity - current_quarter['capacity_used']
+            quarters.append(current_quarter)
+        
+        return quarters
+
+def format_output(features: List[Dict], analysis: Dict, roadmap: List[Dict]) -> str:
+    """Format the results for display"""
+    output = ["=" * 60]
+    output.append("RICE PRIORITIZATION RESULTS")
+    output.append("=" * 60)
+    
+    # Top prioritized features
+    output.append("\n📊 TOP PRIORITIZED FEATURES\n")
+    for i, feature in enumerate(features[:10], 1):
+        output.append(f"{i}. {feature.get('name', 'Unnamed')}")
+        output.append(f"   RICE Score: {feature['rice_score']}")
+        output.append(f"   Reach: {feature.get('reach', 0)} | Impact: {feature.get('impact', 'medium')} | "
+                     f"Confidence: {feature.get('confidence', 'medium')} | Effort: {feature.get('effort', 'm')}")
+        output.append("")
+    
+    # Portfolio analysis
+    output.append("\n📈 PORTFOLIO ANALYSIS\n")
+    output.append(f"Total Features: {analysis.get('total_features', 0)}")
+    output.append(f"Total Effort: {analysis.get('total_effort_months', 0)} person-months")
+    output.append(f"Total Reach: {analysis.get('total_reach', 0):,} users")
+    output.append(f"Average RICE Score: {analysis.get('average_rice', 0)}")
+    
+    output.append(f"\n🎯 Quick Wins: {analysis.get('quick_wins', 0)} features")
+    for qw in analysis.get('quick_wins_list', []):
+        output.append(f"   • {qw.get('name', 'Unnamed')} (RICE: {qw['rice_score']})")
+    
+    output.append(f"\n🚀 Big Bets: {analysis.get('big_bets', 0)} features")
+    for bb in analysis.get('big_bets_list', []):
+        output.append(f"   • {bb.get('name', 'Unnamed')} (RICE: {bb['rice_score']})")
+    
+    # Roadmap
+    output.append("\n\n📅 SUGGESTED ROADMAP\n")
+    for quarter in roadmap:
+        output.append(f"\nQ{quarter['quarter']} - Capacity: {quarter['capacity_used']}/{quarter['capacity_used'] + quarter['capacity_available']} person-months")
+        for feature in quarter['features']:
+            output.append(f"   • {feature.get('name', 'Unnamed')} (RICE: {feature['rice_score']})")
+    
+    return "\n".join(output)
+
+def load_features_from_csv(filepath: str) -> List[Dict]:
+    """Load features from CSV file"""
+    features = []
+    with open(filepath, 'r') as f:
+        reader = csv.DictReader(f)
+        for row in reader:
+            feature = {
+                'name': row.get('name', ''),
+                'reach': int(row.get('reach', 0)),
+                'impact': row.get('impact', 'medium'),
+                'confidence': row.get('confidence', 'medium'),
+                'effort': row.get('effort', 'm'),
+                'description': row.get('description', '')
+            }
+            features.append(feature)
+    return features
+
+def create_sample_csv(filepath: str):
+    """Create a sample CSV file for testing"""
+    sample_features = [
+        ['name', 'reach', 'impact', 'confidence', 'effort', 'description'],
+        ['User Dashboard Redesign', '5000', 'high', 'high', 'l', 'Complete redesign of user dashboard'],
+        ['Mobile Push Notifications', '10000', 'massive', 'medium', 'm', 'Add push notification support'],
+        ['Dark Mode', '8000', 'medium', 'high', 's', 'Implement dark mode theme'],
+        ['API Rate Limiting', '2000', 'low', 'high', 'xs', 'Add rate limiting to API'],
+        ['Social Login', '12000', 'high', 'medium', 'm', 'Add Google/Facebook login'],
+        ['Export to PDF', '3000', 'medium', 'low', 's', 'Export reports as PDF'],
+        ['Team Collaboration', '4000', 'massive', 'low', 'xl', 'Real-time collaboration features'],
+        ['Search Improvements', '15000', 'high', 'high', 'm', 'Enhance search functionality'],
+        ['Onboarding Flow', '20000', 'massive', 'high', 's', 'Improve new user onboarding'],
+        ['Analytics Dashboard', '6000', 'high', 'medium', 'l', 'Advanced analytics for users'],
+    ]
+    
+    with open(filepath, 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerows(sample_features)
+    
+    print(f"Sample CSV created at: {filepath}")
+
+def main():
+    parser = argparse.ArgumentParser(description='RICE Framework for Feature Prioritization')
+    parser.add_argument('input', nargs='?', help='CSV file with features or "sample" to create sample')
+    parser.add_argument('--capacity', type=int, default=10, help='Team capacity per quarter (person-months)')
+    parser.add_argument('--output', choices=['text', 'json', 'csv'], default='text', help='Output format')
+    
+    args = parser.parse_args()
+    
+    # Create sample if requested
+    if args.input == 'sample':
+        create_sample_csv('sample_features.csv')
+        return
+    
+    # Use sample data if no input provided
+    if not args.input:
+        features = [
+            {'name': 'User Dashboard', 'reach': 5000, 'impact': 'high', 'confidence': 'high', 'effort': 'l'},
+            {'name': 'Push Notifications', 'reach': 10000, 'impact': 'massive', 'confidence': 'medium', 'effort': 'm'},
+            {'name': 'Dark Mode', 'reach': 8000, 'impact': 'medium', 'confidence': 'high', 'effort': 's'},
+            {'name': 'API Rate Limiting', 'reach': 2000, 'impact': 'low', 'confidence': 'high', 'effort': 'xs'},
+            {'name': 'Social Login', 'reach': 12000, 'impact': 'high', 'confidence': 'medium', 'effort': 'm'},
+        ]
+    else:
+        features = load_features_from_csv(args.input)
+    
+    # Calculate RICE scores
+    calculator = RICECalculator()
+    prioritized = calculator.prioritize_features(features)
+    analysis = calculator.analyze_portfolio(prioritized)
+    roadmap = calculator.generate_roadmap(prioritized, args.capacity)
+    
+    # Output results
+    if args.output == 'json':
+        result = {
+            'features': prioritized,
+            'analysis': analysis,
+            'roadmap': roadmap
+        }
+        print(json.dumps(result, indent=2))
+    elif args.output == 'csv':
+        # Output prioritized features as CSV
+        if prioritized:
+            keys = prioritized[0].keys()
+            print(','.join(keys))
+            for feature in prioritized:
+                print(','.join(str(feature.get(k, '')) for k in keys))
+    else:
+        print(format_output(prioritized, analysis, roadmap))
+
+if __name__ == "__main__":
+    main()