product-manager-toolkit（产品经理工具包）

Product Manager Toolkit

Overview

This skill provides [TODO: Add 2-3 sentence overview].

Core Value: [TODO: Add value proposition with metrics]

Target Audience: [TODO: Define target users]

Use Cases: [TODO: List 3-5 primary use cases]

Core Capabilities

• [Capability 1] - [Description]
• [Capability 2] - [Description]
• [Capability 3] - [Description]
• [Capability 4] - [Description]

Key Workflows

Workflow 1: [Workflow Name]

Time: [Duration estimate]

Steps:

1. [Step 1]
2. [Step 2]
3. [Step 3]

Expected Output: [What success looks like]

Workflow 2: [Workflow Name]

Time: [Duration estimate]

Steps:

1. [Step 1]
2. [Step 2]
3. [Step 3]

Expected Output: [What success looks like]

Essential tools and frameworks for modern product management, from discovery to delivery. This toolkit provides Python automation tools for prioritization and interview analysis, comprehensive frameworks for decision-making, and battle-tested templates for product documentation.

What This Skill Provides:

• RICE prioritization engine with portfolio analysis
• NLP-based customer interview analyzer
• Complete PRD templates and interview guides
• Discovery frameworks (JTBD, Opportunity Trees)
• Metrics frameworks (North Star, Funnels)

Best For:

• Feature prioritization and roadmap planning
• User research synthesis and insight extraction
• Requirements documentation (PRDs, user stories)
• Discovery planning and stakeholder alignment

Quick Start

Feature Prioritization

python scripts/rice_prioritizer.py sample  # Create sample CSV
python scripts/rice_prioritizer.py sample_features.csv --capacity 15

Interview Analysis

python scripts/customer_interview_analyzer.py interview_transcript.txt

PRD Creation

1. Choose template: Standard, One-Page, Agile Epic, or Feature Brief
2. See templates.md for complete formats
3. Fill sections based on discovery work
4. Review with stakeholders and version control

Core Workflows

1. Feature Prioritization Process

Steps:

1. Gather feature requests (customer feedback, sales, tech debt, strategic)
2. Score with RICE: python scripts/rice_prioritizer.py features.csv

- Reach: Users affected per quarter - Impact: massive/high/medium/low/minimal (3x/2x/1x/0.5x/0.25x) - Confidence: high/medium/low (100%/80%/50%) - Effort: Person-months

1. Analyze portfolio (quick wins vs big bets)
2. Generate roadmap with capacity planning

Detailed Methodology: See frameworks.md for RICE, Value vs Effort Matrix, MoSCoW, and Kano Model.

2. Customer Discovery Process

Steps:

1. Conduct interviews using semi-structured format
2. Analyze insights: python scripts/customer_interview_analyzer.py transcript.txt

- Extracts pain points, feature requests, JTBD, sentiment, themes

1. Synthesize findings across interviews
2. Validate solutions with prototypes

Interview Scripts: See templates.md for complete discovery and validation interview guides.

Discovery Frameworks: See frameworks.md for Customer Interview Guide, Hypothesis Template, and Opportunity Solution Tree.

3. PRD Development Process

Steps:

1. Choose template based on project size:

- 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

1. Structure: Problem → Solution → Success Metrics
2. Collaborate with engineering, design, sales, support

Complete Templates: See templates.md for all PRD formats with examples.

Python Tools

rice_prioritizer.py

RICE framework implementation with portfolio analysis and roadmap generation.

Key Features:

• RICE score calculation
• Portfolio balance (quick wins, big bets, fill-ins, time sinks)
• Quarterly roadmap with capacity planning
• Multiple output formats (text/json/csv)

Usage:

# Basic prioritization
python3 scripts/rice_prioritizer.py features.csv

# With team capacity
python3 scripts/rice_prioritizer.py features.csv --capacity 20

# JSON output for tool integration
python3 scripts/rice_prioritizer.py features.csv --output json -f roadmap.json

CSV Format:

name,reach,impact,confidence,effort
User Dashboard,500,2,0.8,5
API Rate Limiting,1000,2,0.9,3

Complete Documentation: See tools.md for full options, output formats, and integration patterns.

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 and competitor mentions

Usage:

# Analyze interview
python3 scripts/customer_interview_analyzer.py interview.txt

# JSON output for research tools
python3 scripts/customer_interview_analyzer.py interview.txt --output json -f analysis.json

Complete Documentation: See tools.md for full capabilities, output formats, and batch analysis workflows.

Reference Documentation

Frameworks (frameworks.md)

Detailed frameworks and methodologies:

• Prioritization: RICE (detailed), Value vs Effort Matrix, MoSCoW, Kano Model
• Discovery: Customer Interview Guide, Hypothesis Template, Opportunity Solution Tree
• Metrics: North Star Framework, Funnel Analysis (AARRR), Feature Success Metrics, Cohort Analysis

Templates (templates.md)

Complete templates and best practices:

• PRD Templates: Standard, One-Page, Agile Epic, Feature Brief
• Interview Guides: Discovery interviews, solution validation
• Best Practices: Writing PRDs, prioritization, discovery, stakeholder management
• Common Pitfalls: What to avoid and how to fix

Tools (tools.md)

Python tool documentation and integrations:

• rice_prioritizer.py: Complete usage, options, output formats
• customer_interview_analyzer.py: Full capabilities and workflows
• Integration Patterns: Jira, ProductBoard, Amplitude, Figma, Dovetail, Slack
• Platform Setup: Step-by-step for each tool
• Troubleshooting: Common issues and solutions

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

See tools.md for detailed integration workflows and platform-specific setup guides.

Quick Commands

# 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 --output json