Product Manager Toolkit

davila7/claude-code-templates/cli-tool/components/skills/business-marketing/product-manager-toolkit

作者 davila78da17d671b6f無授權條款32K 個星標收錄於 2026年10月8日更新於 2026年10月8日儲存庫今天更新

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.

AI 產生的概覽

面向產品經理的工具包,支援 RICE 功能優先級排序、客戶訪談分析和 PRD 範本。

功能
為產品經理提供功能優先級排序、客戶探索和需求文件撰寫的框架與指令碼。其中 RICE 優先級指令碼可為功能評分並產出組合分析與路線圖輸出,訪談分析指令碼可擷取痛點、功能請求、待辦任務、情感傾向和主題。另附標準、單頁、敏捷史詩和功能簡報等多種 PRD 範本。
適用情境
適用於為功能待辦清單排序優先級、彙整客戶訪談紀錄,或起草產品需求文件。適合產品探索、路線圖規劃和利害關係人對齊等工作。
執行需求
需要 Python 執行兩個隨附指令碼。輸入為 CSV 功能檔案或訪談紀錄文字檔;未說明需要憑證或網路存取。

Product Manager Toolkit

Essential tools and frameworks for modern product management, from discovery to delivery.

Quick Start

For Feature Prioritization

bash
python scripts/rice_prioritizer.py sample  # Create sample CSVpython 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 in sections based on discovery work
  3. Review with stakeholders
  4. Version control in your PM tool

Core Workflows

Feature Prioritization Process

  1. Gather Feature Requests

    • Customer feedback
    • Sales requests
    • Technical debt
    • Strategic initiatives
  2. Score with RICE

    bash
    # Create CSV with: name,reach,impact,confidence,effortpython scripts/rice_prioritizer.py features.csv
    • Reach: Users affected per quarter
    • Impact: massive/high/medium/low/minimal
    • Confidence: high/medium/low
    • Effort: xl/l/m/s/xs (person-months)
  3. Analyze Portfolio

    • Review quick wins vs big bets
    • Check effort distribution
    • Validate against strategy
  4. Generate Roadmap

    • Quarterly capacity planning
    • Dependency mapping
    • Stakeholder alignment

Customer Discovery Process

  1. Conduct Interviews

    • Use semi-structured format
    • Focus on problems, not solutions
    • Record with permission
  2. Analyze Insights

    bash
    python scripts/customer_interview_analyzer.py transcript.txt

    Extracts:

    • Pain points with severity
    • Feature requests with priority
    • Jobs to be done
    • Sentiment analysis
    • Key themes and quotes
  3. Synthesize Findings

    • Group similar pain points
    • Identify patterns across interviews
    • Map to opportunity areas
  4. Validate Solutions

    • Create solution hypotheses
    • Test with prototypes
    • Measure actual vs expected behavior

PRD Development Process

  1. Choose Template

    • 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
  2. Structure Content

    • Problem → Solution → Success Metrics
    • Always include out-of-scope
    • Clear acceptance criteria
  3. Collaborate

    • Engineering for feasibility
    • Design for experience
    • Sales for market validation
    • Support for operational impact

Key Scripts

rice_prioritizer.py

Advanced RICE framework implementation with portfolio analysis.

Features:

  • RICE score calculation
  • Portfolio balance analysis (quick wins vs big bets)
  • Quarterly roadmap generation
  • Team capacity planning
  • Multiple output formats (text/json/csv)

Usage Examples:

bash
# Basic prioritizationpython scripts/rice_prioritizer.py features.csv
# With custom team capacity (person-months per quarter)python scripts/rice_prioritizer.py features.csv --capacity 20
# Output as JSON for integrationpython scripts/rice_prioritizer.py features.csv --output json

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
  • Competitor mentions
  • Key quotes identification

Usage Examples:

bash
# Analyze single interviewpython scripts/customer_interview_analyzer.py interview.txt
# Output as JSON for aggregationpython scripts/customer_interview_analyzer.py interview.txt json

Reference Documents

prd_templates.md

Multiple PRD formats for different contexts:

  1. Standard PRD Template

    • Comprehensive 11-section format
    • Best for major features
    • Includes technical specs
  2. One-Page PRD

    • Concise format for quick alignment
    • Focus on problem/solution/metrics
    • Good for smaller features
  3. Agile Epic Template

    • Sprint-based delivery
    • User story mapping
    • Acceptance criteria focus
  4. Feature Brief

    • Lightweight exploration
    • Hypothesis-driven
    • Pre-PRD phase

Prioritization Frameworks

RICE Framework

Score = (Reach × Impact × Confidence) / Effort
Reach: # of users/quarterImpact:   - Massive = 3x  - High = 2x  - Medium = 1x  - Low = 0.5x  - Minimal = 0.25xConfidence:  - High = 100%  - Medium = 80%  - Low = 50%Effort: Person-months

Value vs Effort Matrix

         Low Effort    High Effort         High     QUICK WINS    BIG BETSValue    [Prioritize]   [Strategic]         Low      FILL-INS      TIME SINKSValue    [Maybe]       [Avoid]

MoSCoW Method

  • Must Have: Critical for launch
  • Should Have: Important but not critical
  • Could Have: Nice to have
  • Won't Have: Out of scope

Discovery Frameworks

Customer Interview Guide

1. Context Questions (5 min)   - Role and responsibilities   - Current workflow   - Tools used
2. Problem Exploration (15 min)   - Pain points   - Frequency and impact   - Current workarounds
3. Solution Validation (10 min)   - Reaction to concepts   - Value perception   - Willingness to pay
4. Wrap-up (5 min)   - Other thoughts   - Referrals   - Follow-up permission

Hypothesis Template

We believe that [building this feature]For [these users]Will [achieve this outcome]We'll know we're right when [metric]

Opportunity Solution Tree

Outcome├── Opportunity 1│   ├── Solution A│   └── Solution B└── Opportunity 2    ├── Solution C    └── Solution D

Metrics & Analytics

North Star Metric Framework

  1. Identify Core Value: What's the #1 value to users?
  2. Make it Measurable: Quantifiable and trackable
  3. Ensure It's Actionable: Teams can influence it
  4. Check Leading Indicator: Predicts business success

Funnel Analysis Template

Acquisition → Activation → Retention → Revenue → Referral
Key Metrics:- Conversion rate at each step- Drop-off points- Time between steps- Cohort variations

Feature Success Metrics

  • Adoption: % of users using feature
  • Frequency: Usage per user per time period
  • Depth: % of feature capability used
  • Retention: Continued usage over time
  • Satisfaction: NPS/CSAT for feature

Best Practices

Writing Great PRDs

  1. Start with the problem, not solution
  2. Include clear success metrics upfront
  3. Explicitly state what's out of scope
  4. Use visuals (wireframes, flows)
  5. Keep technical details in appendix
  6. Version control changes

Effective Prioritization

  1. Mix quick wins with strategic bets
  2. Consider opportunity cost
  3. Account for dependencies
  4. Buffer for unexpected work (20%)
  5. Revisit quarterly
  6. Communicate decisions clearly

Customer Discovery Tips

  1. Ask "why" 5 times
  2. Focus on past behavior, not future intentions
  3. Avoid leading questions
  4. Interview in their environment
  5. Look for emotional reactions
  6. Validate with data

Stakeholder Management

  1. Identify RACI for decisions
  2. Regular async updates
  3. Demo over documentation
  4. Address concerns early
  5. Celebrate wins publicly
  6. Learn from failures openly

Common Pitfalls to Avoid

  1. Solution-First Thinking: Jumping to features before understanding problems
  2. Analysis Paralysis: Over-researching without shipping
  3. Feature Factory: Shipping features without measuring impact
  4. Ignoring Technical Debt: Not allocating time for platform health
  5. Stakeholder Surprise: Not communicating early and often
  6. Metric Theater: Optimizing vanity metrics over real value

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

Quick Commands Cheat Sheet

bash
# Prioritizationpython scripts/rice_prioritizer.py features.csv --capacity 15
# Interview Analysispython scripts/customer_interview_analyzer.py interview.txt
# Create sample datapython scripts/rice_prioritizer.py sample
# JSON outputs for integrationpython scripts/rice_prioritizer.py features.csv --output jsonpython scripts/customer_interview_analyzer.py interview.txt json

來源與署名

來源:davila7/claude-code-templates位於cli-tool/components/skills/business-marketing/product-manager-toolkit提交8da17d6

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