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
- 2026-11-11
- Stars
- ★ 3.3w
- Forks
- 3.9k
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 3
- 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.
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
- Choose template from
references/prd_templates.md - Fill in sections based on discovery work
- Review with stakeholders
- Version control in your PM tool
Core Workflows
Feature Prioritization Process
-
Gather Feature Requests
- Customer feedback
- Sales requests
- Technical debt
- Strategic initiatives
-
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)
-
Analyze Portfolio
- Review quick wins vs big bets
- Check effort distribution
- Validate against strategy
-
Generate Roadmap
- Quarterly capacity planning
- Dependency mapping
- Stakeholder alignment
Customer Discovery Process
-
Conduct Interviews
- Use semi-structured format
- Focus on problems, not solutions
- Record with permission
-
Analyze Insights
python scripts/customer_interview_analyzer.py transcript.txtExtracts:
- Pain points with severity
- Feature requests with priority
- Jobs to be done
- Sentiment analysis
- Key themes and quotes
-
Synthesize Findings
- Group similar pain points
- Identify patterns across interviews
- Map to opportunity areas
-
Validate Solutions
- Create solution hypotheses
- Test with prototypes
- Measure actual vs expected behavior
PRD Development Process
-
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
-
Structure Content
- Problem → Solution → Success Metrics
- Always include out-of-scope
- Clear acceptance criteria
-
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:
-
Standard PRD Template
- Comprehensive 11-section format
- Best for major features
- Includes technical specs
-
One-Page PRD
- Concise format for quick alignment
- Focus on problem/solution/metrics
- Good for smaller features
-
Agile Epic Template
- Sprint-based delivery
- User story mapping
- Acceptance criteria focus
-
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
- Identify Core Value: What's the #1 value to users?
- Make it Measurable: Quantifiable and trackable
- Ensure It's Actionable: Teams can influence it
- 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
- Start with the problem, not solution
- Include clear success metrics upfront
- Explicitly state what's out of scope
- Use visuals (wireframes, flows)
- Keep technical details in appendix
- Version control changes
Effective Prioritization
- Mix quick wins with strategic bets
- Consider opportunity cost
- Account for dependencies
- Buffer for unexpected work (20%)
- Revisit quarterly
- Communicate decisions clearly
Customer Discovery Tips
- Ask "why" 5 times
- Focus on past behavior, not future intentions
- Avoid leading questions
- Interview in their environment
- Look for emotional reactions
- Validate with data
Stakeholder Management
- Identify RACI for decisions
- Regular async updates
- Demo over documentation
- Address concerns early
- Celebrate wins publicly
- Learn from failures openly
Common Pitfalls to Avoid
- Solution-First Thinking: Jumping to features before understanding problems
- Analysis Paralysis: Over-researching without shipping
- Feature Factory: Shipping features without measuring impact
- Ignoring Technical Debt: Not allocating time for platform health
- Stakeholder Surprise: Not communicating early and often
- 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
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