product-manager-toolkit
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.
- License 许可
- MIT
- 最近更新
- 2026-03-08
- Stars
- ★ 2
- Forks
- 0
01 前言 Front Matter
- 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.
02 资源文件 4
- 参考文档references/frameworks.md16.4 KB
- 参考文档references/prd_templates.md7.1 KB
- 脚本scripts/customer_interview_analyzer.py16.7 KB
- 脚本scripts/rice_prioritizer.py11.6 KB
03 SKILL.md 正文
Product Manager Toolkit
Essential tools and frameworks for modern product management, from discovery to delivery.
Table of Contents
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:
| 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
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
RICE Prioritizer Example
Input (features.csv):
name,reach,impact,confidence,effort
Onboarding Flow,20000,massive,high,s
Search Improvements,15000,high,high,m
Social Login,12000,high,medium,m
Push Notifications,10000,massive,medium,m
Dark Mode,8000,medium,high,s
Command:
python scripts/rice_prioritizer.py features.csv --capacity 15
Output:
============================================================
RICE PRIORITIZATION RESULTS
============================================================
? TOP PRIORITIZED FEATURES
1. Onboarding Flow
RICE Score: 16000.0
Reach: 20000 | Impact: massive | Confidence: high | Effort: s
2. Search Improvements
RICE Score: 4800.0
Reach: 15000 | Impact: high | Confidence: high | Effort: m
3. Social Login
RICE Score: 3072.0
Reach: 12000 | Impact: high | Confidence: medium | Effort: m
4. Push Notifications
RICE Score: 3840.0
Reach: 10000 | Impact: massive | Confidence: medium | Effort: m
5. Dark Mode
RICE Score: 2133.33
Reach: 8000 | Impact: medium | Confidence: high | Effort: s
? PORTFOLIO ANALYSIS
Total Features: 5
Total Effort: 19 person-months
Total Reach: 65,000 users
Average RICE Score: 5969.07
? Quick Wins: 2 features
• Onboarding Flow (RICE: 16000.0)
• Dark Mode (RICE: 2133.33)
? Big Bets: 0 features
? SUGGESTED ROADMAP
Q1 - Capacity: 11/15 person-months
• Onboarding Flow (RICE: 16000.0)
• Search Improvements (RICE: 4800.0)
• Dark Mode (RICE: 2133.33)
Q2 - Capacity: 10/15 person-months
• Push Notifications (RICE: 3840.0)
• Social Login (RICE: 3072.0)
Customer Interview Analyzer Example
Input (interview.txt):
Customer: Jane, Enterprise PM at TechCorp
Date: 2024-01-15
Interviewer: What's the hardest part of your current workflow?
Jane: The biggest frustration is the lack of real-time collaboration.
When I'm working on a PRD, I have to constantly ping my team on Slack
to get updates. It's really frustrating to wait for responses,
especially when we're on a tight deadline.
I've tried using Google Docs for collaboration, but it doesn't
integrate with our roadmap tools. I'd pay extra for something that
just worked seamlessly.
Interviewer: How often does this happen?
Jane: Literally every day. I probably waste 30 minutes just on
back-and-forth messages. It's my biggest pain point right now.
Command:
python scripts/customer_interview_analyzer.py interview.txt
Output:
============================================================
CUSTOMER INTERVIEW ANALYSIS
============================================================
? INTERVIEW METADATA
Segments found: 1
Lines analyzed: 15
? PAIN POINTS (3 found)
1. [HIGH] Lack of real-time collaboration
"I have to constantly ping my team on Slack to get updates"
2. [MEDIUM] Tool integration gaps
"Google Docs...doesn't integrate with our roadmap tools"
3. [HIGH] Time wasted on communication
"waste 30 minutes just on back-and-forth messages"
? FEATURE REQUESTS (2 found)
1. Real-time collaboration - Priority: High
2. Seamless tool integration - Priority: Medium
? JOBS TO BE DONE
When working on PRDs with tight deadlines
I want real-time visibility into team updates
So I can avoid wasted time on status checks
? SENTIMENT ANALYSIS
Overall: Negative (pain-focused interview)
Key emotions: Frustration, Time pressure
? KEY QUOTES
• "It's really frustrating to wait for responses"
• "I'd pay extra for something that just worked seamlessly"
• "It's my biggest pain point right now"
?️ THEMES
- Collaboration friction
- Tool fragmentation
- Time efficiency
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
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 |
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
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
Reference Documents
references/prd_templates.md- PRD templates for different contextsreferences/frameworks.md- Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)
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