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product-manager-toolkit

sickn33/antigravity-awesome-skills

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

What is product-manager-toolkit?

A toolkit providing scripts, templates, and frameworks for product managers to prioritize features, analyze customer interviews, and create product requirements documents. Use it to systematize feature prioritization with RICE scoring, extract insights from customer conversations, and align stakeholders around product decisions.

  • Prioritize features using the RICE framework (Reach, Impact, Confidence, Effort) with portfolio analysis and quarterly roadmap generation
  • Analyze customer interview transcripts to extract pain points, feature requests, jobs-to-be-done, sentiment, and key themes
  • Generate and manage PRD templates in multiple formats (Standard, One-Page, Agile Epic, Feature Brief)
  • Plan team capacity and identify quick wins vs. strategic bets using value-effort matrices
  • Track feature success with adoption, frequency, retention, and satisfaction metrics

How to install product-manager-toolkit

npx skills add https://github.com/sickn33/antigravity-awesome-skills --skill product-manager-toolkit
Prerequisites
  • Python 3.7+ installed
  • CSV files with feature data (for RICE prioritizer) or interview transcripts (for analyzer)
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How to use product-manager-toolkit

  1. 1.Run `python scripts/rice_prioritizer.py sample` to create a sample CSV template
  2. 2.Populate the CSV with your features, reach, impact, confidence, and effort estimates
  3. 3.Execute `python scripts/rice_prioritizer.py your_features.csv --capacity [person-months]` to generate prioritized roadmap
  4. 4.For customer insights, run `python scripts/customer_interview_analyzer.py transcript.txt` on interview transcripts
  5. 5.Review extracted pain points, feature requests, and themes in the output
  6. 6.Select a PRD template from `references/prd_templates.md` matching your feature scope and timeline
  7. 7.Fill in PRD sections based on discovery findings and share with engineering, design, and sales for feedback

Use cases

Good for
  • Quarterly roadmap planning: score all feature requests with RICE, analyze portfolio balance, and allocate team capacity
  • Customer discovery synthesis: run interview transcripts through the analyzer to identify patterns and validate solution hypotheses
  • PRD creation for engineering handoff: select appropriate template, fill sections based on discovery work, and version control with stakeholders
  • Stakeholder alignment: use prioritization output and metrics frameworks to communicate product decisions and trade-offs
  • Hypothesis validation: structure discovery work with customer interview guides and opportunity solution trees before building
Who it's for
  • Product managers building roadmaps and managing stakeholder expectations
  • Product teams conducting customer discovery and validating hypotheses
  • Engineering leaders collaborating on feature prioritization and capacity planning
  • Startup founders making build-vs-buy decisions with limited resources

product-manager-toolkit FAQ

What CSV format does the RICE prioritizer expect?

A CSV with columns: name, reach, impact, confidence, effort. Reach is a number of users per quarter; impact is one of (massive/high/medium/low/minimal); confidence is (high/medium/low); effort is person-months (xl/l/m/s/xs).

Can I use the customer interview analyzer on multiple interviews?

Yes. Run the script on each transcript individually, then manually aggregate the outputs to identify patterns across interviews, or output as JSON for programmatic aggregation.

Which PRD template should I use?

Use Standard PRD for complex features (6-8 weeks), One-Page PRD for simple features (2-4 weeks), Feature Brief for exploration phase (1 week), or Agile Epic for sprint-based delivery.

How do I handle features with dependencies in prioritization?

The toolkit supports dependency mapping in roadmap generation. Score each feature independently with RICE, then use the portfolio analysis to sequence features and identify blocking dependencies.

What if I don't have confidence data for a feature?

Use 'Low' confidence (50%) as a conservative estimate. The RICE framework penalizes low-confidence items, which is appropriate for unvalidated features. Conduct customer discovery to increase confidence before committing resources.

Full instructions (SKILL.md)

Source of truth, from sickn33/antigravity-awesome-skills.


name: product-manager-toolkit description: "Essential tools and frameworks for modern product management, from discovery to delivery." risk: unknown source: community date_added: "2026-02-27"

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

  1. Choose template from references/prd_templates.md
  2. Fill in sections based on discovery work
  3. Review with stakeholders
  4. Version control in your PM tool

Core Workflows

Feature Prioritization Process

  1. Gather Feature Requests

    • Customer feedback
    • Sales requests
    • Technical debt
    • Strategic initiatives
  2. 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)
  3. Analyze Portfolio

    • Review quick wins vs big bets
    • Check effort distribution
    • Validate against strategy
  4. Generate Roadmap

    • Quarterly capacity planning
    • Dependency mapping
    • Stakeholder alignment

Customer Discovery Process

  1. Conduct Interviews

    • Use semi-structured format
    • Focus on problems, not solutions
    • Record with permission
  2. Analyze Insights

    python scripts/customer_interview_analyzer.py transcript.txt
    

    Extracts:

    • Pain points with severity
    • Feature requests with priority
    • Jobs to be done
    • Sentiment analysis
    • Key themes and quotes
  3. Synthesize Findings

    • Group similar pain points
    • Identify patterns across interviews
    • Map to opportunity areas
  4. Validate Solutions

    • Create solution hypotheses
    • Test with prototypes
    • Measure actual vs expected behavior

PRD Development Process

  1. 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
  2. Structure Content

    • Problem → Solution → Success Metrics
    • Always include out-of-scope
    • Clear acceptance criteria
  3. 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:

  1. Standard PRD Template

    • Comprehensive 11-section format
    • Best for major features
    • Includes technical specs
  2. One-Page PRD

    • Concise format for quick alignment
    • Focus on problem/solution/metrics
    • Good for smaller features
  3. Agile Epic Template

    • Sprint-based delivery
    • User story mapping
    • Acceptance criteria focus
  4. 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

  1. Identify Core Value: What's the #1 value to users?
  2. Make it Measurable: Quantifiable and trackable
  3. Ensure It's Actionable: Teams can influence it
  4. 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

  1. Start with the problem, not solution
  2. Include clear success metrics upfront
  3. Explicitly state what's out of scope
  4. Use visuals (wireframes, flows)
  5. Keep technical details in appendix
  6. Version control changes

Effective Prioritization

  1. Mix quick wins with strategic bets
  2. Consider opportunity cost
  3. Account for dependencies
  4. Buffer for unexpected work (20%)
  5. Revisit quarterly
  6. Communicate decisions clearly

Customer Discovery Tips

  1. Ask "why" 5 times
  2. Focus on past behavior, not future intentions
  3. Avoid leading questions
  4. Interview in their environment
  5. Look for emotional reactions
  6. Validate with data

Stakeholder Management

  1. Identify RACI for decisions
  2. Regular async updates
  3. Demo over documentation
  4. Address concerns early
  5. Celebrate wins publicly
  6. Learn from failures openly

Common Pitfalls to Avoid

  1. Solution-First Thinking: Jumping to features before understanding problems
  2. Analysis Paralysis: Over-researching without shipping
  3. Feature Factory: Shipping features without measuring impact
  4. Ignoring Technical Debt: Not allocating time for platform health
  5. Stakeholder Surprise: Not communicating early and often
  6. 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

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.