claude-skills-reference/product-team/CLAUDE.md

Product Team Skills - Claude Code Guidance

This guide covers the 8 production-ready product management skills and their Python automation tools.

Product Skills Overview

Available Skills:

1. product-manager-toolkit/ - RICE prioritization, customer interview analysis (2 tools)
2. agile-product-owner/ - User story generation, sprint planning (1 tool)
3. product-strategist/ - OKR cascade, strategic planning (1 tool)
4. ux-researcher-designer/ - Persona generation, user research (1 tool)
5. ui-design-system/ - Design token generation, component systems (1 tool)
6. competitive-teardown/ - Competitive matrix building, gap analysis (1 tool)
7. landing-page-generator/ - Landing page scaffolding (1 tool)
8. saas-scaffolder/ - SaaS project bootstrapping (1 tool)

Total Tools: 9 Python automation tools

Agents: 4 (cs-product-manager, cs-agile-product-owner, cs-product-strategist, cs-ux-researcher)

Slash Commands: 5 (/rice, /okr, /persona, /user-story, /competitive-matrix)

Python Automation Tools

1. RICE Prioritizer (product-manager-toolkit/scripts/rice_prioritizer.py)

Purpose: RICE framework implementation for feature prioritization

Formula: (Reach × Impact × Confidence) / Effort

Features:

• Portfolio analysis (quick wins vs big bets)
• Quarterly roadmap generation
• Capacity planning (story points or dev days)
• CSV input/output for Jira/Linear integration
• JSON export for dashboards

Usage:

# Basic prioritization
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv

# With capacity planning
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv --capacity 20

# JSON output
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv --output json

CSV Format:

feature,reach,impact,confidence,effort
User Dashboard,500,3,0.8,5
API Rate Limiting,1000,2,0.9,3
Dark Mode,300,1,1.0,2

2. Customer Interview Analyzer (product-manager-toolkit/scripts/customer_interview_analyzer.py)

Purpose: NLP-based interview transcript analysis

Features:

• Pain point extraction with severity scoring
• Feature request identification
• Sentiment analysis
• Theme extraction
• Jobs-to-be-done pattern recognition

Usage:

# Analyze transcript
python product-manager-toolkit/scripts/customer_interview_analyzer.py interview.txt

# JSON output
python product-manager-toolkit/scripts/customer_interview_analyzer.py interview.txt json

3. User Story Generator (agile-product-owner/scripts/user_story_generator.py)

Purpose: INVEST-compliant user story generation

Features:

• Sprint planning with capacity allocation
• Epic breakdown into deliverable stories
• Acceptance criteria generation
• Story point estimation
• Priority scoring

Usage:

# Interactive mode
python agile-product-owner/scripts/user_story_generator.py

# Sprint planning (30 story points)
python agile-product-owner/scripts/user_story_generator.py sprint 30

Output Format:

US-001: As a user, I want to...
Priority: High | Points: 5
Acceptance Criteria:
- Given... When... Then...

4. OKR Cascade Generator (product-strategist/scripts/okr_cascade_generator.py)

Purpose: Automated OKR hierarchy (company → product → team)

Features:

• Alignment scoring (vertical and horizontal)
• Strategy templates (growth, retention, revenue, innovation)
• Key result tracking
• Progress visualization

Usage:

# Growth strategy OKRs
python product-strategist/scripts/okr_cascade_generator.py growth

# Retention strategy
python product-strategist/scripts/okr_cascade_generator.py retention

5. Persona Generator (ux-researcher-designer/scripts/persona_generator.py)

Purpose: Data-driven persona creation from user research

Features:

• Demographic and psychographic profiling
• Goals, pain points, and behavior patterns
• User journey mapping integration
• Empathy map generation

Usage:

# Interactive persona creation
python ux-researcher-designer/scripts/persona_generator.py

# JSON export
python ux-researcher-designer/scripts/persona_generator.py --output json

6. Design Token Generator (ui-design-system/scripts/design_token_generator.py)

Purpose: Complete design token system from brand color

Features:

• Color palette generation (primary, secondary, neutrals)
• Typography scale (font sizes, line heights, weights)
• Spacing system (4px/8px grid)
• Shadow and elevation tokens
• Export formats: CSS, JSON, SCSS

Usage:

# Generate design tokens
python ui-design-system/scripts/design_token_generator.py "#0066CC" modern css

# SCSS output
python ui-design-system/scripts/design_token_generator.py "#0066CC" modern scss

# JSON for Figma integration
python ui-design-system/scripts/design_token_generator.py "#0066CC" modern json

7. Competitive Matrix Builder (competitive-teardown/scripts/competitive_matrix_builder.py)

Purpose: Weighted competitive scoring with gap analysis

Usage:

python competitive-teardown/scripts/competitive_matrix_builder.py competitors.json

8. Landing Page Scaffolder (landing-page-generator/scripts/landing_page_scaffolder.py)

Purpose: Generate production-ready landing pages as Next.js/React TSX components with Tailwind CSS (default) or plain HTML.

Features:

• TSX output (default): Next.js 14+ App Router components with Tailwind classes
• 4 design styles: dark-saas, clean-minimal, bold-startup, enterprise
• 7 section generators: nav, hero, features, testimonials, pricing, CTA, footer
• Copy frameworks: PAS, AIDA, BAB
• SEO metadata export
• HTML output preserved via --format html

Usage:

# TSX output (default) with design style
python landing-page-generator/scripts/landing_page_scaffolder.py config.json --format tsx

# HTML output
python landing-page-generator/scripts/landing_page_scaffolder.py config.json --format html

# JSON manifest (dry run)
python landing-page-generator/scripts/landing_page_scaffolder.py config.json --format json

Config JSON format:

{
  "product_name": "Acme",
  "tagline": "Ship faster. Break less.",
  "design_style": "dark-saas",
  "copy_framework": "PAS",
  "sections": ["nav", "hero", "features", "pricing", "cta", "footer"],
  "features": [
    {"title": "Fast deploys", "description": "Zero-downtime deployments"}
  ],
  "pricing": [
    {"name": "Free", "price": "$0/mo", "features": ["5 projects"]},
    {"name": "Pro", "price": "$29/mo", "features": ["Unlimited"], "highlighted": true}
  ]
}

Brand Voice Integration: Before generating copy, run the brand voice analyzer to establish tone and formality:

# 1. Analyze existing brand content to establish voice profile
python ../marketing-skill/content-production/scripts/brand_voice_analyzer.py brand_samples.txt --format json > voice_profile.json

# 2. Use the voice profile (formality, tone, perspective) to guide copy framework selection
# 3. Generate landing page with matching style
python landing-page-generator/scripts/landing_page_scaffolder.py config.json --format tsx

9. Project Bootstrapper (saas-scaffolder/scripts/project_bootstrapper.py)

Purpose: SaaS project scaffolding with auth, billing, and API setup

Usage:

python saas-scaffolder/scripts/project_bootstrapper.py project_config.json

Product Workflows

Workflow 1: Feature Prioritization

# 1. Collect feature requests
cat feature-requests.csv

# 2. Run RICE prioritization
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv --capacity 30

# 3. Generate quarterly roadmap

# 4. Create user stories for top priorities
python agile-product-owner/scripts/user_story_generator.py sprint 30

Workflow 2: User Research to Product

# 1. Conduct user interviews

# 2. Analyze transcripts
python product-manager-toolkit/scripts/customer_interview_analyzer.py interview-001.txt

# 3. Generate personas
python ux-researcher-designer/scripts/persona_generator.py

# 4. Create OKRs based on insights
python product-strategist/scripts/okr_cascade_generator.py growth

Workflow 3: Sprint Planning

# 1. Set sprint capacity (story points)
CAPACITY=30

# 2. Generate user stories
python agile-product-owner/scripts/user_story_generator.py sprint $CAPACITY

# 3. Export to Jira (via JSON)
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv --output json > priorities.json

Workflow 4: Brand-Aligned Landing Page

This workflow connects the marketing brand voice skill with the landing page generator to ensure copy consistency.

# 1. Analyze existing brand content for voice profile
python ../marketing-skill/content-production/scripts/brand_voice_analyzer.py website_copy.txt --format json > voice.json
# Output: formality (formal/casual), tone (professional/friendly), perspective (authoritative/conversational)

# 2. Map voice profile to design style + copy framework:
#    - formal + professional → enterprise style, AIDA framework
#    - casual + friendly → bold-startup style, BAB framework
#    - professional + authoritative → dark-saas style, PAS framework
#    - casual + conversational → clean-minimal style, BAB framework

# 3. Generate design tokens for brand consistency
python ui-design-system/scripts/design_token_generator.py "#0066CC" modern css

# 4. Generate the landing page
python landing-page-generator/scripts/landing_page_scaffolder.py config.json --format tsx

# 5. Run competitive teardown to refine positioning
python competitive-teardown/scripts/competitive_matrix_builder.py competitors.json

Integration Patterns

Jira Integration

All tools support JSON output for Jira import:

# Export prioritized features
python product-manager-toolkit/scripts/rice_prioritizer.py features.csv --output json > jira-import.json

Figma Integration

Design tokens export for Figma plugins:

# Generate tokens
python ui-design-system/scripts/design_token_generator.py "#0066CC" modern json > design-tokens.json

Confluence Documentation

Use persona generator output for user documentation:

python ux-researcher-designer/scripts/persona_generator.py --output json > personas.json

Quality Standards

All product Python tools must:

• CLI-first design for automation
• Support both interactive and batch modes
• JSON output for tool integration
• Standard library only (minimal dependencies)
• Actionable recommendations

Cross-Domain Integration

Brand Voice → Landing Page

The landing page generator integrates with the marketing brand voice analyzer (marketing-skill/content-production/scripts/brand_voice_analyzer.py) to ensure copy on generated pages matches the brand's established voice. The analyzer outputs formality, tone, and perspective dimensions which map to design style and copy framework choices. See Workflow 4 above.

Design Tokens → Landing Page

Design tokens from ui-design-system/scripts/design_token_generator.py can be generated alongside landing pages to ensure consistent color, typography, and spacing across the product.

Competitive Teardown → Landing Page

Competitive positioning from competitive-teardown/scripts/competitive_matrix_builder.py informs landing page messaging — use SWOT analysis to identify differentiation points and translate them into hero copy and feature sections.

Additional Resources

• Main Documentation: ../CLAUDE.md
• Marketing Brand Voice: ../marketing-skill/content-production/scripts/brand_voice_analyzer.py

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Last Updated: March 10, 2026 Skills Deployed: 8/8 product skills production-ready Total Tools: 9 Python automation tools Agents: 4 | Commands: 5