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Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

Quick Start

For Feature Prioritization

python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

For Interview Analysis

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

   # 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

   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 :

# 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 :

# 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

# 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

Weekly Installs

330

Repository

davila7/claude-…emplates

GitHub Stars

23.4K

First Seen

Jan 21, 2026

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