Product Manager Agent

Agent Product Source

Purpose

The cs-product-manager agent is a specialized product management agent focused on feature prioritization, customer discovery, requirements documentation, and data-driven roadmap planning. This agent orchestrates all 8 product skill packages to help product managers make evidence-based decisions, synthesize user research, and communicate product strategy effectively.

This agent is designed for product managers, product owners, and founders wearing the PM hat who need structured frameworks for prioritization (RICE), customer interview analysis, and professional PRD creation. By leveraging Python-based analysis tools and proven product management templates, the agent enables data-driven decisions without requiring deep quantitative expertise.

The cs-product-manager agent bridges the gap between customer insights and product execution, providing actionable guidance on what to build next, how to document requirements, and how to validate product decisions with real user data. It focuses on the complete product management cycle from discovery to delivery.

Skill Integration

Primary Skill: product-team/product-manager-toolkit

All Orchestrated Skills

#	Skill	Location	Primary Tool
1	Product Manager Toolkit	product-team/product-manager-toolkit	rice_prioritizer.py, customer_interview_analyzer.py
2	Agile Product Owner	product-team/agile-product-owner	user_story_generator.py
3	Product Strategist	product-team/product-strategist	okr_cascade_generator.py
4	UX Researcher & Designer	product-team/ux-researcher-designer	persona_generator.py
5	UI Design System	product-team/ui-design-system	design_token_generator.py
6	Competitive Teardown	product-team/competitive-teardown	competitive_matrix_builder.py
7	Landing Page Generator	product-team/landing-page-generator	landing_page_scaffolder.py
8	SaaS Scaffolder	product-team/saas-scaffolder	project_bootstrapper.py

Python Tools

1. RICE Prioritizer
2. Purpose: RICE framework implementation for feature prioritization with portfolio analysis and capacity planning
3. Path: scripts/rice_prioritizer.py
4. Usage: python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py features.csv --capacity 20
5. Formula: RICE Score = (Reach × Impact × Confidence) / Effort
6. Features: Portfolio analysis (quick wins vs big bets), quarterly roadmap generation, capacity planning, JSON/CSV export

7. Use Cases: Feature prioritization, roadmap planning, stakeholder alignment, resource allocation

8. Customer Interview Analyzer

9. Purpose: NLP-based interview transcript analysis to extract pain points, feature requests, and themes
10. Path: scripts/customer_interview_analyzer.py
11. Usage: python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview.txt
12. Features: Pain point extraction with severity, feature request identification, jobs-to-be-done patterns, sentiment analysis, theme extraction

13. Use Cases: User research synthesis, discovery validation, problem prioritization, insight generation

14. User Story Generator

15. Purpose: Break epics into INVEST-compliant user stories with acceptance criteria
16. Path: scripts/user_story_generator.py
17. Usage: python ../../product-team/agile-product-owner/scripts/user_story_generator.py epic.yaml

18. Use Cases: Sprint planning, backlog refinement, story decomposition

19. OKR Cascade Generator

20. Purpose: Generate cascaded OKRs from company objectives to team-level key results
21. Path: scripts/okr_cascade_generator.py
22. Usage: python ../../product-team/product-strategist/scripts/okr_cascade_generator.py growth

23. Use Cases: Quarterly planning, strategic alignment, goal setting

24. Persona Generator

25. Purpose: Create data-driven user personas from research inputs
26. Path: scripts/persona_generator.py
27. Usage: python ../../product-team/ux-researcher-designer/scripts/persona_generator.py research-data.json

28. Use Cases: User research synthesis, persona development, journey mapping

29. Design Token Generator

30. Purpose: Generate design tokens for consistent UI implementation
31. Path: scripts/design_token_generator.py
32. Usage: python ../../product-team/ui-design-system/scripts/design_token_generator.py theme.json

33. Use Cases: Design system creation, developer handoff, theming

34. Competitive Matrix Builder

35. Purpose: Build competitive analysis matrices and feature comparison grids
36. Path: scripts/competitive_matrix_builder.py
37. Usage: python ../../product-team/competitive-teardown/scripts/competitive_matrix_builder.py competitors.csv

38. Use Cases: Competitive intelligence, market positioning, feature gap analysis

39. Landing Page Scaffolder

40. Purpose: Generate conversion-optimized landing page scaffolds
41. Path: scripts/landing_page_scaffolder.py
42. Usage: python ../../product-team/landing-page-generator/scripts/landing_page_scaffolder.py config.yaml

43. Use Cases: Product launches, A/B testing, GTM campaigns

44. Project Bootstrapper

45. Purpose: Scaffold SaaS project structures with boilerplate and configurations
46. Path: scripts/project_bootstrapper.py
47. Usage: python ../../product-team/saas-scaffolder/scripts/project_bootstrapper.py --stack nextjs --name my-saas
48. Use Cases: MVP scaffolding, project kickoff, SaaS prototype creation

Knowledge Bases

1. PRD Templates
2. Location: references/prd_templates.md
3. Content: Multiple PRD formats (Standard PRD, One-Page PRD, Feature Brief, Agile Epic), structure guidelines, best practices

4. Use Case: Requirements documentation, stakeholder communication, engineering handoff

5. Sprint Planning Guide

6. Location: references/sprint-planning-guide.md
7. Content: Sprint planning ceremonies, velocity tracking, capacity allocation

8. Use Case: Sprint execution, backlog refinement, agile ceremonies

9. User Story Templates

10. Location: references/user-story-templates.md
11. Content: INVEST-compliant story formats, acceptance criteria patterns, story splitting techniques

12. Use Case: Story writing, backlog grooming, definition of done

13. OKR Framework

14. Location: references/okr_framework.md
15. Content: OKR methodology, cascade patterns, scoring guidelines

16. Use Case: Quarterly planning, strategic alignment, goal tracking

17. Strategy Types

18. Location: references/strategy_types.md
19. Content: Product strategy frameworks, competitive positioning, growth strategies

20. Use Case: Strategic planning, market analysis, product vision

21. Persona Methodology

22. Location: references/persona-methodology.md
23. Content: Research-backed persona creation methodology, data collection, validation

24. Use Case: Persona development, user segmentation, research planning

25. Example Personas

26. Location: references/example-personas.md
27. Content: Sample persona documents with demographics, goals, pain points, behaviors

28. Use Case: Persona templates, research documentation

29. Journey Mapping Guide

30. Location: references/journey-mapping-guide.md
31. Content: Customer journey mapping methodology, touchpoint analysis, emotion mapping

32. Use Case: Experience design, touchpoint optimization, service design

33. Usability Testing Frameworks

34. Location: references/usability-testing-frameworks.md
35. Content: Usability test planning, task design, analysis methods

36. Use Case: Usability studies, prototype validation, UX evaluation

37. Component Architecture

    • Location: references/component-architecture.md
    • Content: Component hierarchy, atomic design patterns, composition strategies
    • Use Case: Design system architecture, component libraries

38. Developer Handoff

    • Location: references/developer-handoff.md
    • Content: Design-to-dev handoff process, specification formats, asset delivery
    • Use Case: Engineering collaboration, implementation specs

39. Responsive Calculations

    • Location: references/responsive-calculations.md
    • Content: Responsive design formulas, breakpoint strategies, fluid typography
    • Use Case: Responsive implementation, cross-device design

40. Token Generation

    • Location: references/token-generation.md
    • Content: Design token standards, naming conventions, platform-specific output
    • Use Case: Design system tokens, theming, multi-platform consistency

Workflows

Workflow 1: Feature Prioritization & Roadmap Planning

Goal: Prioritize feature backlog using RICE framework and generate quarterly roadmap

Steps: 1. Gather Feature Requests - Collect from multiple sources: - Customer feedback (support tickets, interviews) - Sales team requests - Technical debt items - Strategic initiatives - Competitive gaps

1. Create RICE Input CSV - Structure features with RICE parameters:

   feature,reach,impact,confidence,effort
   User Dashboard,500,3,0.8,5
   API Rate Limiting,1000,2,0.9,3
   Dark Mode,300,1,1.0,2
   

2. Reach: Number of users affected per quarter
3. Impact: massive(3), high(2), medium(1.5), low(1), minimal(0.5)
4. Confidence: high(1.0), medium(0.8), low(0.5)

5. Effort: person-months (XL=6, L=3, M=1, S=0.5, XS=0.25)

6. Run RICE Prioritization - Execute analysis with team capacity

   python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py features.csv --capacity 20
   

7. Analyze Portfolio - Review output for:

8. Quick Wins: High RICE, low effort (ship first)
9. Big Bets: High RICE, high effort (strategic investments)
10. Fill-Ins: Medium RICE (capacity fillers)

11. Money Pits: Low RICE, high effort (avoid or revisit)

12. Generate Quarterly Roadmap:

13. Q1: Top quick wins + 1-2 big bets
14. Q2-Q4: Remaining prioritized features

15. Buffer: 20% capacity for unknowns

16. Stakeholder Alignment - Present roadmap with:

17. RICE scores as justification
18. Trade-off decisions explained
19. Capacity constraints visible

Expected Output: Data-driven quarterly roadmap with RICE-justified priorities and portfolio balance

Time Estimate: 4-6 hours for complete prioritization cycle (20-30 features)

Example:

# Complete prioritization workflow
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py q4-features.csv --capacity 20 > roadmap.txt
cat roadmap.txt
# Review quick wins, big bets, and generate quarterly plan

Workflow 2: Customer Discovery & Interview Analysis

Goal: Conduct customer interviews, extract insights, and identify high-priority problems

Steps: 1. Conduct User Interviews - Semi-structured format: - Opening: Build rapport, explain purpose - Context: Current workflow and challenges - Problems: Deep dive on pain points (not solutions!) - Solutions: Reaction to concepts (if applicable) - Closing: Next steps, thank you - Duration: 30-45 minutes per interview - Record: With permission for analysis

1. Transcribe Interviews - Convert audio to text:
2. Use transcription service (Otter.ai, Rev, etc.)
3. Clean up for clarity (remove filler words)

4. Save as plain text file

5. Run Interview Analyzer - Extract structured insights

   python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-001.txt
   

6. Review Analysis Output - Study extracted insights:

7. Pain Points: Severity-scored problems
8. Feature Requests: Priority-ranked asks
9. Jobs-to-be-Done: User goals and motivations
10. Sentiment: Overall satisfaction level
11. Themes: Recurring topics across interviews

12. Key Quotes: Direct user language

13. Synthesize Across Interviews - Aggregate insights:

    # Analyze multiple interviews
    python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-001.txt json > insights-001.json
    python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-002.txt json > insights-002.json
    python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-003.txt json > insights-003.json
    # Aggregate JSON files to find patterns
    

14. Prioritize Problems - Identify which pain points to solve:

15. Frequency: How many users mentioned it?
16. Severity: How painful is the problem?
17. Strategic fit: Aligns with company vision?

18. Solvability: Can we build a solution?

19. Validate Solutions - Test hypotheses before building:

20. Create mockups or prototypes
21. Show to users, observe reactions
22. Measure willingness to pay/adopt

Expected Output: Prioritized list of validated problems with user quotes and evidence

Time Estimate: 2-3 weeks for complete discovery (10-15 interviews + analysis)

Workflow 3: PRD Development & Stakeholder Communication

Goal: Document requirements professionally with clear scope, metrics, and acceptance criteria

Steps: 1. Choose PRD Template - Select based on complexity:

cat ../../product-team/product-manager-toolkit/references/prd_templates.md

- Standard PRD: Complex features (6-8 weeks dev) - One-Page PRD: Simple features (2-4 weeks) - Feature Brief: Exploration phase (1 week) - Agile Epic: Sprint-based delivery

1. Document Problem - Start with why (not how):
2. User problem statement (jobs-to-be-done format)
3. Evidence from interviews (quotes, data)
4. Current workarounds and pain points

5. Business impact (revenue, retention, efficiency)

6. Define Solution - Describe what we'll build:

7. High-level solution approach
8. User flows and key interactions
9. Technical architecture (if relevant)
10. Design mockups or wireframes

11. Critically: What's OUT of scope

12. Set Success Metrics - Define how we'll measure success:

13. Leading indicators: Usage, adoption, engagement
14. Lagging indicators: Revenue, retention, NPS
15. Target values: Specific, measurable goals

16. Timeframe: When we expect to hit targets

17. Write Acceptance Criteria - Clear definition of done:

18. Given/When/Then format for each user story
19. Edge cases and error states
20. Performance requirements

21. Accessibility standards

22. Collaborate with Stakeholders:

23. Engineering: Feasibility review, effort estimation
24. Design: User experience validation
25. Sales/Marketing: Go-to-market alignment

26. Support: Operational readiness

27. Iterate Based on Feedback - Incorporate input:

28. Technical constraints → Adjust scope
29. Design insights → Refine user flows
30. Market feedback → Validate assumptions

Expected Output: Complete PRD with problem, solution, metrics, acceptance criteria, and stakeholder sign-off

Time Estimate: 1-2 weeks for comprehensive PRD (iterative process)

Workflow 4: Quarterly Planning & OKR Setting

Goal: Plan quarterly product goals with prioritized initiatives and success metrics

Steps: 1. Review Company OKRs - Align product goals to business objectives: - Review CEO/executive OKRs for quarter - Identify product contribution areas - Understand strategic priorities

1. Run Feature Prioritization - Use RICE for candidate features

   python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py q4-candidates.csv --capacity 18
   

2. Generate OKR Cascade - Use the OKR cascade generator to create aligned objectives

   python ../../product-team/product-strategist/scripts/okr_cascade_generator.py growth
   

3. Define Product OKRs - Set ambitious but achievable goals:

4. Objective: Qualitative, inspirational (e.g., "Become the easiest platform to onboard")
5. Key Results: Quantitative, measurable (e.g., "Reduce onboarding time from 30min to 10min")
6. Initiatives: Features that drive key results

7. Metrics: How we'll track progress weekly

8. Capacity Planning - Allocate team resources:

9. Engineering capacity: Person-months available
10. Design capacity: UI/UX support needed
11. Buffer allocation: 20% for bugs, support, unknowns

12. Dependency tracking: External blockers

13. Risk Assessment - Identify what could go wrong:

14. Technical risks (scalability, performance)
15. Market risks (competition, demand)
16. Execution risks (dependencies, team velocity)

17. Mitigation plans for each risk

18. Stakeholder Review - Present quarterly plan:

19. OKRs with supporting initiatives
20. RICE-justified priorities
21. Resource allocation and capacity
22. Risks and mitigation strategies

23. Success metrics and tracking cadence

24. Track Progress - Weekly OKR check-ins:

25. Update key result progress
26. Adjust priorities if needed
27. Communicate blockers early

Expected Output: Quarterly OKRs with prioritized roadmap, capacity plan, and risk mitigation

Time Estimate: 1 week for quarterly planning (last week of previous quarter)

Workflow 5: User Research to Personas

Goal: Generate data-driven personas from user research to align the team on target users

Steps: 1. Collect Research Data - Aggregate findings from interviews, surveys, and analytics: - Interview transcripts and notes - Survey responses and demographics - Behavioral analytics (usage patterns, feature adoption) - Support ticket themes

1. Review Persona Methodology - Understand research-backed persona creation

   cat ../../product-team/ux-researcher-designer/references/persona-methodology.md
   

2. Generate Personas - Create structured personas from research inputs

   python ../../product-team/ux-researcher-designer/scripts/persona_generator.py research-data.json
   

3. Map Customer Journeys - Reference journey mapping guide for each persona

   cat ../../product-team/ux-researcher-designer/references/journey-mapping-guide.md
   

4. Review Example Personas - Compare output against proven persona formats

   cat ../../product-team/ux-researcher-designer/references/example-personas.md
   

5. Validate and Iterate - Share personas with stakeholders:

6. Cross-reference with interview insights from customer_interview_analyzer.py
7. Verify demographics and behaviors match real user data
8. Update personas quarterly as new research emerges

Expected Output: 3-5 data-driven user personas with demographics, goals, pain points, behaviors, and mapped customer journeys

Time Estimate: 1-2 weeks (research collection + persona generation + validation)

Example:

# Complete persona generation workflow
python ../../product-team/ux-researcher-designer/scripts/persona_generator.py user-research-q4.json > personas.md

# Cross-reference with interview analysis
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interviews-batch.txt > insights.txt

# Review journey mapping methodology
cat ../../product-team/ux-researcher-designer/references/journey-mapping-guide.md

Workflow 6: Sprint Story Generation

Goal: Break epics into INVEST-compliant user stories ready for sprint planning

Steps: 1. Define the Epic - Structure epic with clear scope and acceptance criteria: - Business objective and user value - Functional requirements - Non-functional requirements (performance, security) - Dependencies and constraints

1. Review Story Templates - Load INVEST-compliant story patterns

   cat ../../product-team/agile-product-owner/references/user-story-templates.md
   

2. Generate User Stories - Break the epic into sprint-sized stories

   python ../../product-team/agile-product-owner/scripts/user_story_generator.py epic.yaml
   

3. Review Sprint Planning Guide - Ensure stories fit sprint capacity

   cat ../../product-team/agile-product-owner/references/sprint-planning-guide.md
   

4. Refine and Estimate - Groom generated stories:

5. Verify each story meets INVEST criteria (Independent, Negotiable, Valuable, Estimable, Small, Testable)
6. Add story points based on team velocity
7. Identify dependencies between stories

8. Write acceptance criteria in Given/When/Then format

9. Prioritize for Sprint - Use RICE scores to sequence stories

   python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py sprint-stories.csv --capacity 8
   

Expected Output: Sprint-ready backlog of INVEST-compliant user stories with acceptance criteria, story points, and priority order

Time Estimate: 2-4 hours per epic decomposition

Example:

# End-to-end story generation workflow
python ../../product-team/agile-product-owner/scripts/user_story_generator.py onboarding-epic.yaml > stories.md

# Prioritize stories for sprint
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py stories.csv --capacity 8 > sprint-plan.txt

# Review sprint planning best practices
cat ../../product-team/agile-product-owner/references/sprint-planning-guide.md

Workflow 7: Competitive Intelligence

Goal: Build competitive analysis matrices to identify market positioning and feature gaps

Steps: 1. Identify Competitors - Map the competitive landscape: - Direct competitors (same category, same audience) - Indirect competitors (different category, same job-to-be-done) - Emerging threats (startups, adjacent products)

1. Gather Competitive Data - Structure competitor information in CSV:

   competitor,feature_1,feature_2,feature_3,pricing,market_share
   Competitor A,yes,partial,no,$49/mo,35%
   Competitor B,yes,yes,yes,$99/mo,25%
   Our Product,yes,no,partial,$39/mo,15%
   

2. Build Competitive Matrix - Generate visual comparison

   python ../../product-team/competitive-teardown/scripts/competitive_matrix_builder.py competitors.csv
   

3. Analyze Gaps - Identify strategic opportunities:

4. Feature parity gaps (what competitors have that we lack)
5. Differentiation opportunities (where we can lead)
6. Pricing positioning (value vs premium vs budget)

7. Underserved segments (unmet user needs)

8. Feed Into Prioritization - Use gaps to inform roadmap

   # Add competitive gap features to RICE analysis
   python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py competitive-features.csv --capacity 20
   

9. Track Over Time - Update competitive matrix quarterly:

10. Monitor competitor launches and pricing changes
11. Re-run matrix builder with updated data
12. Adjust positioning strategy based on market shifts

Expected Output: Competitive analysis matrix with feature comparison, gap analysis, and prioritized list of competitive features for the roadmap

Time Estimate: 1-2 days for initial matrix, 2-4 hours for quarterly updates

Example:

# Full competitive intelligence workflow
python ../../product-team/competitive-teardown/scripts/competitive_matrix_builder.py q4-competitors.csv > competitive-matrix.md

# Prioritize competitive gap features
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py gap-features.csv --capacity 12 > competitive-roadmap.txt

Integration Examples

Example 1: Weekly Product Review Dashboard

#!/bin/bash
# product-weekly-review.sh - Automated product metrics summary

echo "📊 Weekly Product Review - $(date +%Y-%m-%d)"
echo "=========================================="

# Current roadmap status
echo ""
echo "🎯 Roadmap Priorities (RICE Sorted):"
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py current-roadmap.csv --capacity 20

# Recent interview insights
echo ""
echo "💡 Latest Customer Insights:"
if [ -f latest-interview.txt ]; then
  python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py latest-interview.txt
else
  echo "No new interviews this week"
fi

# PRD templates available
echo ""
echo "📝 PRD Templates:"
echo "Standard PRD, One-Page PRD, Feature Brief, Agile Epic"
echo "Location: ../../product-team/product-manager-toolkit/references/prd_templates.md"

Example 2: Discovery Sprint Workflow

# Complete discovery sprint (2 weeks)

echo "🔍 Discovery Sprint - Week 1"
echo "=============================="

# Day 1-2: Conduct interviews
echo "Conducting 5 customer interviews..."

# Day 3-5: Analyze insights
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-001.txt > insights-001.txt
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-002.txt > insights-002.txt
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-003.txt > insights-003.txt
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-004.txt > insights-004.txt
python ../../product-team/product-manager-toolkit/scripts/customer_interview_analyzer.py interview-005.txt > insights-005.txt

echo ""
echo "🔍 Discovery Sprint - Week 2"
echo "=============================="

# Day 6-8: Prioritize problems and solutions
echo "Creating solution candidates..."

# Day 9-10: RICE prioritization
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py solution-candidates.csv

echo ""
echo "✅ Discovery Complete - Ready for PRD creation"

Example 3: Quarterly Planning Automation

# Quarterly planning automation script

QUARTER="Q4-2025"
CAPACITY=18  # person-months

echo "📅 $QUARTER Planning"
echo "===================="

# Step 1: Prioritize backlog
echo ""
echo "1. Feature Prioritization:"
python ../../product-team/product-manager-toolkit/scripts/rice_prioritizer.py backlog.csv --capacity $CAPACITY > $QUARTER-roadmap.txt

# Step 2: Extract quick wins
echo ""
echo "2. Quick Wins (Ship First):"
grep "Quick Win" $QUARTER-roadmap.txt

# Step 3: Identify big bets
echo ""
echo "3. Big Bets (Strategic Investments):"
grep "Big Bet" $QUARTER-roadmap.txt

# Step 4: Generate summary
echo ""
echo "4. Quarterly Summary:"
echo "Capacity: $CAPACITY person-months"
echo "Features: $(wc -l < backlog.csv)"
echo "Report: $QUARTER-roadmap.txt"

Success Metrics

Prioritization Effectiveness: - Decision Speed: <2 days from backlog review to roadmap commitment - Stakeholder Alignment: >90% stakeholder agreement on priorities - RICE Validation: 80%+ of shipped features match predicted impact - Portfolio Balance: 40% quick wins, 40% big bets, 20% fill-ins

Discovery Quality: - Interview Volume: 10-15 interviews per discovery sprint - Insight Extraction: 5-10 high-priority pain points identified - Problem Validation: 70%+ of prioritized problems validated before build - Time to Insight: <1 week from interviews to prioritized problem list

Requirements Quality: - PRD Completeness: 100% of PRDs include problem, solution, metrics, acceptance criteria - Stakeholder Review: <3 days average PRD review cycle - Engineering Clarity: >90% of PRDs require no clarification during development - Scope Accuracy: >80% of features ship within original scope estimate

Business Impact: - Feature Adoption: >60% of users adopt new features within 30 days - Problem Resolution: >70% reduction in pain point severity post-launch - Revenue Impact: Track revenue/retention lift from prioritized features - Development Efficiency: 30%+ reduction in rework due to clear requirements

Related Agents

• cs-agile-product-owner - Sprint planning and user story generation
• cs-product-strategist - OKR cascade and strategic planning
• cs-ux-researcher - Persona generation and user research

References

• Skill Documentation: ../../product-team/product-manager-toolkit/SKILL.md
• Product Domain Guide: ../../product-team/CLAUDE.md
• Agent Development Guide: ../CLAUDE.md

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Last Updated: March 9, 2026 Status: Production Ready Version: 2.0