Product Manager Toolkit

What This Skill Does

The Product Manager Toolkit provides essential tools and frameworks for modern product management, covering discovery to delivery. It offers scripts for feature prioritization using RICE and customer interview analysis, along with PRD templates. This skill is designed for product managers seeking to streamline their workflows and make data-informed decisions.

When to Use

• Prioritizing features using the RICE method.
• Analyzing customer interview transcripts.
• Creating product requirements documents (PRDs).Gathering feature requests from various sources.
• Analyzing portfolio distribution of features.
• Generating product roadmaps with capacity allocation.

Key Features

• Includes a RICE prioritizer script.
• Offers a customer interview analyzer script.
• Provides PRD templates for documentation.
• Supports quick wins vs big bets analysis.
• Helps identify strategic alignment gaps.
• Offers sample data for quick starts.

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

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

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

Choose template from references/prd_templates.md Fill sections based on discovery work Review with engineering for feasibility Version control in project management tool

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

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

PRD Development Process

Scope → Draft → Review → Refine → Approve → Track

Step 1: Choose Template

Select from references/prd_templates.md:

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

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

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

Input/Output Examples

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

Integration Points

Compatible tools and platforms:

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

Common Pitfalls to Avoid