openclaw-skills/product-manager-toolkit/SKILL.md
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.
npx skillsauth add seaworld008/commonly-used-high-value-skills product-manager-toolkitInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Essential tools and frameworks for modern product management, from discovery to delivery.
# 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
python scripts/customer_interview_analyzer.py interview_transcript.txt
references/prd_templates.mdGather → Score → Analyze → Plan → Validate → Execute
# Input: CSV with features
python scripts/rice_prioritizer.py features.csv --capacity 20
See references/frameworks.md for RICE formula and scoring guidelines.
Review the tool output for:
Before finalizing the roadmap:
Plan → Recruit → Interview → Analyze → Synthesize → Validate
references/frameworks.md)python scripts/customer_interview_analyzer.py transcript.txt
Extracts:
Before building:
references/frameworks.md)Scope → Draft → Review → Refine → Approve → Track
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 |
After launch:
Advanced RICE framework implementation with portfolio analysis.
Features:
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
NLP-based interview analysis for extracting actionable insights.
Capabilities:
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 (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)
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
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
| 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 |
Writing Great PRDs:
Effective Prioritization:
Customer Discovery:
# 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
references/prd_templates.md - PRD templates for different contextsreferences/frameworks.md - Detailed framework documentation (RICE, MoSCoW, Kano, JTBD, etc.)development
Enumerating failure modes via pre-mortem analysis. Systematically identifies failure scenarios for plans, designs, and features, scoring them with RPN/AP. Does not write code.
testing
Orchestrating specialist AI agent teams as a meta-coordinator. Decomposes requests into minimum viable chains, spawns each as an independent session in AUTORUN modes, and drives to final output. Use when a task spans multiple specialist domains, requires parallel agent execution, or needs hub-and-spoke routing across the skill ecosystem.
development
Converting document formats (Markdown/Word/Excel/PDF/HTML). Converts specs from Scribe and reports from Harvest into distributable formats; generates reusable conversion scripts. Use when converting documents, building accessibility-compliant PDFs, or creating Pandoc/LibreOffice pipelines.
testing
Curating cross-agent knowledge and guarding institutional memory. Extracts patterns from agent journals into METAPATTERNS.md, detects knowledge decay, propagates best practices, prevents organizational forgetting. Use when consolidating cross-agent insights, curating memory, or auditing knowledge decay.