Product Manager Toolkit

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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.

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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.)

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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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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