Product Manager Toolkit

Domain: Product | Skill: product-manager-toolkit | Source: product-team/product-manager-toolkit/SKILL.md

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Product Manager Toolkit

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

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Table of Contents

• Quick Start
• Core Workflows
• Feature Prioritization
• Customer Discovery
• PRD Development
• Tools Reference
• RICE Prioritizer
• Customer Interview Analyzer
• Input/Output Examples
• Integration Points
• Common Pitfalls

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Quick Start

For Feature Prioritization

# Create sample data file
python scripts/rice_prioritizer.py sample

# Run prioritization with team capacity
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 sections based on discovery work
3. Review with engineering for feasibility
4. Version control in project management tool

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Core Workflows

Feature Prioritization Process

Gather → Score → Analyze → Plan → Validate → Execute

Step 1: Gather Feature Requests

• Customer feedback (support tickets, interviews)
• Sales requests (CRM pipeline blockers)
• Technical debt (engineering input)
• Strategic initiatives (leadership goals)

Step 2: Score with RICE

# Input: CSV with features
python scripts/rice_prioritizer.py features.csv --capacity 20

See references/frameworks.md for RICE formula and scoring guidelines.

Step 3: Analyze Portfolio

Review the tool output for: - Quick wins vs big bets distribution - Effort concentration (avoid all XL projects) - Strategic alignment gaps

Step 4: Generate Roadmap

• Quarterly capacity allocation
• Dependency identification
• Stakeholder communication plan

Step 5: Validate Results

Before finalizing the roadmap: - [ ] Compare top priorities against strategic goals - [ ] Run sensitivity analysis (what if estimates are wrong by 2x?) - [ ] Review with key stakeholders for blind spots - [ ] Check for missing dependencies between features - [ ] Validate effort estimates with engineering

Step 6: Execute and Iterate

• Share roadmap with team
• Track actual vs estimated effort
• Revisit priorities quarterly
• Update RICE inputs based on learnings

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Customer Discovery Process

Plan → Recruit → Interview → Analyze → Synthesize → Validate

Step 1: Plan Research

• Define research questions
• Identify target segments
• Create interview script (see references/frameworks.md)

Step 2: Recruit Participants

• 5-8 interviews per segment
• Mix of power users and churned users
• Incentivize appropriately

Step 3: Conduct Interviews

• Use semi-structured format
• Focus on problems, not solutions
• Record with permission
• Take minimal notes during interview

Step 4: Analyze Insights

python scripts/customer_interview_analyzer.py transcript.txt

Extracts: - Pain points with severity - Feature requests with priority - Jobs to be done patterns - Sentiment and key themes - Notable quotes

Step 5: Synthesize Findings

• Group similar pain points across interviews
• Identify patterns (3+ mentions = pattern)
• Map to opportunity areas using Opportunity Solution Tree
• Prioritize opportunities by frequency and severity

Step 6: Validate Solutions

Before building: - [ ] Create solution hypotheses (see references/frameworks.md) - [ ] Test with low-fidelity prototypes - [ ] Measure actual behavior vs stated preference - [ ] Iterate based on feedback - [ ] Document learnings for future research

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PRD Development Process

Scope → Draft → Review → Refine → Approve → Track

Step 1: Choose Template

Select from references/prd_templates.md:

Template	Use Case	Timeline
Standard PRD	Complex features, cross-team	6-8 weeks
One-Page PRD	Simple features, single team	2-4 weeks
Feature Brief	Exploration phase	1 week
Agile Epic	Sprint-based delivery	Ongoing

Step 2: Draft Content

• Lead with problem statement
• Define success metrics upfront
• Explicitly state out-of-scope items
• Include wireframes or mockups

Step 3: Review Cycle

• Engineering: feasibility and effort
• Design: user experience gaps
• Sales: market validation
• Support: operational impact

Step 4: Refine Based on Feedback

• Address technical constraints
• Adjust scope to fit timeline
• Document trade-off decisions

Step 5: Approval and Kickoff

• Stakeholder sign-off
• Sprint planning integration
• Communication to broader team

Step 6: Track Execution

After launch: - [ ] Compare actual metrics vs targets - [ ] Conduct user feedback sessions - [ ] Document what worked and what didn't - [ ] Update estimation accuracy data - [ ] Share learnings with team

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Tools Reference

RICE Prioritizer

Advanced RICE framework implementation with portfolio analysis.

Features: - RICE score calculation with configurable weights - Portfolio balance analysis (quick wins vs big bets) - Quarterly roadmap generation based on capacity - Multiple output formats (text, JSON, CSV)

CSV Input Format:

name,reach,impact,confidence,effort,description
User Dashboard Redesign,5000,high,high,l,Complete redesign
Mobile Push Notifications,10000,massive,medium,m,Add push support
Dark Mode,8000,medium,high,s,Dark theme option

Commands:

# Create sample data
python scripts/rice_prioritizer.py sample

# Run with default capacity (10 person-months)
python scripts/rice_prioritizer.py features.csv

# Custom capacity
python scripts/rice_prioritizer.py features.csv --capacity 20

# JSON output for integration
python scripts/rice_prioritizer.py features.csv --output json

# CSV output for spreadsheets
python scripts/rice_prioritizer.py features.csv --output csv

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Customer Interview Analyzer

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 per section - Theme and quote extraction - Competitor mention detection

Commands:

# Analyze interview transcript
python scripts/customer_interview_analyzer.py interview.txt

# JSON output for aggregation
python scripts/customer_interview_analyzer.py interview.txt json

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Input/Output Examples

→ See references/input-output-examples.md for details

Integration Points

Compatible tools and platforms:

Category	Platforms
Analytics	Amplitude, Mixpanel, Google Analytics
Roadmapping	ProductBoard, Aha!, Roadmunk, Productplan
Design	Figma, Sketch, Miro
Development	Jira, Linear, GitHub, Asana
Research	Dovetail, UserVoice, Pendo, Maze
Communication	Slack, Notion, Confluence

JSON export enables integration with most tools:

# Export for Jira import
python scripts/rice_prioritizer.py features.csv --output json > priorities.json

# Export for dashboard
python scripts/customer_interview_analyzer.py interview.txt json > insights.json

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Common Pitfalls to Avoid

Pitfall	Description	Prevention
Solution-First	Jumping to features before understanding problems	Start every PRD with problem statement
Analysis Paralysis	Over-researching without shipping	Set time-boxes for research phases
Feature Factory	Shipping features without measuring impact	Define success metrics before building
Ignoring Tech Debt	Not allocating time for platform health	Reserve 20% capacity for maintenance
Stakeholder Surprise	Not communicating early and often	Weekly async updates, monthly demos
Metric Theater	Optimizing vanity metrics over real value	Tie metrics to user value delivered

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Best Practices

Writing Great PRDs: - Start with the problem, not the solution - Include clear success metrics upfront - Explicitly state what's out of scope - Use visuals (wireframes, flows, diagrams) - Keep technical details in appendix - Version control all changes

Effective Prioritization: - Mix quick wins with strategic bets - Consider opportunity cost of delays - Account for dependencies between features - Buffer 20% for unexpected work - Revisit priorities quarterly - Communicate decisions with context

Customer Discovery: - Ask "why" five times to find root cause - Focus on past behavior, not future intentions - Avoid leading questions ("Wouldn't you love...") - Interview in the user's natural environment - Watch for emotional reactions (pain = opportunity) - Validate qualitative with quantitative data

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Quick Reference

# Prioritization
python scripts/rice_prioritizer.py features.csv --capacity 15

# Interview Analysis
python scripts/customer_interview_analyzer.py interview.txt

# Generate sample data
python scripts/rice_prioritizer.py sample

# JSON outputs
python scripts/rice_prioritizer.py features.csv --output json
python scripts/customer_interview_analyzer.py interview.txt json

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Reference Documents

• references/prd_templates.md - PRD templates for different contexts
• references/frameworks.md - Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)