— 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. — # Product Manager Toolkit Essential tools and frameworks for modern product management, from discovery to delivery. — ## Table of Contents – [Quick Start](#quick-start) – [Core Workflows](#core-workflows) – [Feature Prioritization](#feature-prioritization-process) – [Customer Discovery](#customer-discovery-process) – [PRD Development](#prd-development-process) – [Tools Reference](#tools-reference) – [RICE Prioritizer](#rice-prioritizer) – [Customer Interview Analyzer](#customer-interview-analyzer) – [Input/Output Examples](#inputoutput-examples) – [Integration Points](#integration-points) – [Common Pitfalls](#common-pitfalls-to-avoid) — ## Quick Start ### For Feature Prioritization “`bash # 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 “`bash python scripts/customer_interview_analyzer.py interview_transcript.txt “` ### For PRD Creation 1. Choose template from `references/prd_templates.md` 2. Fill sections based on discovery work 3. Review with engineering for feasibility 4. 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 “`bash # 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 “`bash 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 — ## 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:** “`csv 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:** “`bash # 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:** “`bash # 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 → See references/input-output-examples.md for details ## 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:** “`bash # 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 “`bash # 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 contexts – `references/frameworks.md` – Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)