product-manager-toolkit
Originally fromsickn33/antigravity-awesome-skills
Installation
SKILL.md
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
Positioning Statement Framework
Create a Geoffrey Moore-style positioning statement to clarify product differentiation and value. Use this before writing PRDs, go-to-market plans, or pitch decks.
Core Positioning Template
For [target user/persona]
who [underserved need or painful moment],
[product name] is a [product category]
that [primary outcome delivered].
Unlike [main alternative: competitor, workaround, or status quo],
[product name] [unique differentiation in outcome terms].
One-Sentence Value Proposition
Write a single sentence a PM can reuse in docs and slides.
Differentiation Proof Points
List 3 concrete proof points that support the "unlike" claim. Focus on outcomes and evidence, not adjectives.
Writing Rules
- Use persona-first language.
- Focus on outcomes, not feature lists.
- Keep wording specific and testable.
- "Unlike X" should name the real alternative, including status quo.
- Strong differentiation is about outcomes and evidence, not adjectives.
Optional Variants
- Executive variant: Shorter strategic wording for board decks.
- Customer-facing variant: Clear plain-language wording for marketing.
Next Steps
- Generate 3 alternate positioning directions (Recommended)
- Create a competitor comparison message matrix
- Convert into homepage headline + subheadline options
Recommendation Canvas
Evaluate product opportunities holistically using a structured canvas that connects problem framing to solution evidence. Useful for investment decisions, portfolio reviews, and stakeholder alignment.
Canvas Sections
## Product Name
[Name of the product or service]
## Business Outcome
[Direction] [Metric] [Outcome] [Context] [Acceptance criteria]
## Product Outcome
[Direction] [Metric] [Outcome] [Context] [Acceptance criteria]
## Problem Statement Narrative
[2-3 sentences telling the persona's story from their point-of-view]
## Solution Hypothesis
If we [action/solution] for [target persona],
then we will [desirable outcome].
### Tiny Acts of Discovery
- [Small experiment focused on viability]
- [Small experiment focused on customer value]
### Proof-of-Life
Within [timeframe], we observe:
- [Quantitative measurable outcome]
- [Qualitative measurable outcome]
## Positioning Statement
For [target persona] that need [underserved need],
[product] is a [category] that [benefit].
Unlike [competitor], [product] provides [differentiation].
## Assumptions & Unknowns
- [Assumption 1]
- [Assumption 2]
## Issues/Risks (PESTEL lens)
- Political: [Risk]
- Economic: [Risk]
- Social: [Risk]
- Technological: [Risk]
- Environmental: [Risk]
- Legal: [Risk]
## Value Justification
[Yes/Yes with caveats/No with alternatives/No]
Justification: [Why this is or isn't valuable]
## Success Metrics
1. [SMART metric 1]
2. [SMART metric 2]
3. [SMART metric 3]
## What's Next
1. [Next step with owner]
2. [Next step with owner]
When to Use
- Evaluating whether to invest in a new product or feature.
- Preparing for portfolio review or investment committee.
- Aligning stakeholders on go/no-go decisions.
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.)
Tool Reference
rice_prioritizer.py
RICE framework implementation with portfolio analysis and quarterly roadmap generation.
| Flag | Type | Default | Description |
|---|---|---|---|
input |
positional | (optional) | CSV file with features or "sample" to create sample |
--capacity |
int | 10 | Team capacity per quarter in person-months |
--output |
choice | text | Output format: text, json, csv |
CSV columns: name, reach, impact, confidence, effort, description
Impact values: massive, high, medium, low, minimal Confidence values: high (100%), medium (80%), low (50%) Effort values: xl (13mo), l (8mo), m (5mo), s (3mo), xs (1mo)
python scripts/rice_prioritizer.py sample # Create sample CSV
python scripts/rice_prioritizer.py features.csv # Default capacity (10)
python scripts/rice_prioritizer.py features.csv --capacity 20 # Custom capacity
python scripts/rice_prioritizer.py features.csv --output json # JSON for integration
python scripts/rice_prioritizer.py features.csv --output csv # CSV for spreadsheets
customer_interview_analyzer.py
Keyword-based interview transcript analysis for extracting actionable insights.
| Argument | Type | Default | Description |
|---|---|---|---|
interview_file |
positional | (required) | Path to interview transcript text file |
json |
positional | (optional) | Add "json" as second arg for JSON output |
Extraction capabilities: pain points (with severity), feature requests (with type and priority), jobs-to-be-done patterns, sentiment analysis, key themes, notable quotes, metrics mentioned, competitor mentions.
python scripts/customer_interview_analyzer.py interview.txt # Human-readable
python scripts/customer_interview_analyzer.py interview.txt json # JSON output
Troubleshooting
| Problem | Cause | Solution |
|---|---|---|
| RICE scores cluster together | Impact/confidence not differentiated enough | Calibrate scoring rubric with team; use specific examples for each level |
| Roadmap overcommits capacity | Effort estimates too optimistic | Add 20% buffer; validate estimates with engineering before finalizing |
| Interview analysis misses key insights | Transcript is too short or uses unexpected phrasing | Supplement with manual review; ensure transcripts capture full context |
| Stakeholders disagree with priorities | Different value perceptions | Share raw RICE inputs transparently; allow stakeholders to adjust weights |
| Quick wins dominate roadmap | Bias toward low-effort items | Reserve 30-40% of capacity for strategic big bets |
| PRD scope creeps after approval | Insufficient out-of-scope definition | Explicitly list excluded items; require change request for additions |
| Feature factory behavior | Shipping without measuring impact | Define success metrics in PRD before development starts |
Success Criteria
| Criterion | Target | How to Measure |
|---|---|---|
| Prioritization velocity | <2 hours from data to ranked backlog | Time from CSV input to roadmap output |
| Interview analysis coverage | >80% of pain points captured | Compare tool output to manual expert review |
| Estimation accuracy | Actual effort within 1.5x of RICE estimate | Track actual vs estimated effort post-delivery |
| Roadmap confidence | >70% of Q1 roadmap items shipped in quarter | Shipped items / Planned items |
| Discovery cadence | 5-8 interviews per segment per quarter | Count completed interviews |
| PRD quality | 0 scope change requests after approval | Track change requests per PRD |
| Feature impact rate | >60% of shipped features hit success metrics | Post-launch metric comparison |
Scope & Limitations
In scope:
- RICE prioritization with portfolio analysis
- Quarterly roadmap generation with capacity planning
- Customer interview transcript analysis
- Pain point, feature request, and JTBD extraction
- Sentiment analysis using keyword heuristics
- PRD development process and templates
- CSV/JSON import and export
Out of scope:
- Real-time analytics integration (use Amplitude/Mixpanel APIs)
- NLP model-based analysis (tool uses keyword heuristics, not ML)
- Multi-language transcript analysis (English only)
- Visual wireframe or prototype generation
- Competitive intelligence gathering (see business-growth skills)
- Revenue impact modeling (see finance skills)
Integration Points
| Tool / Platform | Integration Method | Use Case |
|---|---|---|
| Jira / Linear | --output json from rice_prioritizer |
Import prioritized features as tickets |
| Google Sheets | --output csv from rice_prioritizer |
Share roadmap with stakeholders |
| Dovetail / Notion | JSON output from interview analyzer | Aggregate interview insights in research repo |
| agile-product-owner | RICE priorities feed sprint backlog | Connect strategy to execution |
| product-strategist | OKR cascade informs RICE reach/impact | Align features with strategic objectives |
| Slack / Email | Human-readable output from both tools | Async stakeholder communication |