
product-manager-toolkit
Use when the user needs product management workflows such as RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, go-to-market strategy, feature prioritization, research synthesis, or requirements documentation.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/majiayu000/spellbook --skill product-manager-toolkit- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads product-manager-toolkit automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
Product Manager Toolkit Skill
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
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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
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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
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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
FAQ
What does the product-manager-toolkit skill do?
Use when the user needs product management workflows such as RICE prioritization, customer interview analysis, PRD templates, discovery frameworks, go-to-market strategy, feature prioritization, research synthesis, or requirements documentation.
How do I install product-manager-toolkit?
Run npx skills add https://github.com/majiayu000/spellbook --skill product-manager-toolkit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/product-manager-toolkit/ (personal) or .claude/skills/product-manager-toolkit/ (project).
Which AI agents can use product-manager-toolkit?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is product-manager-toolkit safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains product-manager-toolkit?
It is published by majiayu000 in majiayu000/spellbook. It has 76 recorded installs.
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