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- OpenClaw
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- Manual import
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- Extract archive
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- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Product Manager Toolkit
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.
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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.
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- Tencent SkillHub
- What's included
- SKILL.md, references/frameworks.md, references/input-output-examples.md, references/prd_templates.md, scripts/customer_interview_analyzer.py, scripts/rice_prioritizer.py
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Release facts
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- Tencent SkillHub
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- Indexed source record
- Version
- 2.1.1
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Documentation
Product Manager Toolkit
Essential tools and frameworks for modern product management, from discovery to delivery.
Table of Contents
Quick Start Core Workflows Feature Prioritization Customer Discovery PRD Development Tools Reference RICE Prioritizer Customer Interview Analyzer Input/Output Examples Integration Points Common Pitfalls
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
Feature Prioritization Process
Gather โ Score โ Analyze โ Plan โ Validate โ 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 โ Recruit โ Interview โ Analyze โ Synthesize โ 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 โ Draft โ Review โ Refine โ Approve โ Track Step 1: Choose Template Select from references/prd_templates.md: TemplateUse CaseTimelineStandard PRDComplex features, cross-team6-8 weeksOne-Page PRDSimple features, single team2-4 weeksFeature BriefExploration phase1 weekAgile EpicSprint-based deliveryOngoing 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
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
โ See references/input-output-examples.md for details
Integration Points
Compatible tools and platforms: CategoryPlatformsAnalyticsAmplitude, Mixpanel, Google AnalyticsRoadmappingProductBoard, Aha!, Roadmunk, ProductplanDesignFigma, Sketch, MiroDevelopmentJira, Linear, GitHub, AsanaResearchDovetail, UserVoice, Pendo, MazeCommunicationSlack, 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
PitfallDescriptionPreventionSolution-FirstJumping to features before understanding problemsStart every PRD with problem statementAnalysis ParalysisOver-researching without shippingSet time-boxes for research phasesFeature FactoryShipping features without measuring impactDefine success metrics before buildingIgnoring Tech DebtNot allocating time for platform healthReserve 20% capacity for maintenanceStakeholder SurpriseNot communicating early and oftenWeekly async updates, monthly demosMetric TheaterOptimizing vanity metrics over real valueTie 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 contexts references/frameworks.md - Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- references/frameworks.md
- references/input-output-examples.md
- references/prd_templates.md
- scripts/customer_interview_analyzer.py
- scripts/rice_prioritizer.py