agentleFS
lizandroclizandroc/claude-skills/product-team/CLAUDE.md

claude-skills / product-team

CLAUDE.md1 starsChanged 12 months agoMITView on GitHub

This guide covers the 5 production-ready product management skills and their Python automation tools. 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) Total Tools: 6 Python automation tools Purpose: RICE framework implementation for feature prioritization Formula: (Reach ×…

We split this CLAUDE.md into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    All tools support JSON output for Jira import:

    line 209
  2. Distinctive:

    Use persona generator output for user documentation:

    line 227

≈ “Common” is an estimate: at least 1 in 5 of the 25,680 CLAUDE.md files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
CLAUDE.md

Product Team Skills - Claude Code Guidance

This guide covers the 5 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)

Total Tools: 6 Python automation tools

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

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

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

Roadmap

Current (Phase 1): 5 skills deployed with 6 tools

Phase 2 (Q1 2026): Product analytics

  • A/B test analyzer
  • Funnel conversion tracker
  • Cohort retention analyzer

Phase 3 (Q2 2026): Advanced PM tools

  • Competitive analysis framework
  • Product-market fit assessment
  • Revenue impact calculator

See product_team_implementation_guide.md for detailed plans.

Additional Resources

  • Implementation Guide: product_team_implementation_guide.md
  • Real-World Scenario: REAL_WORLD_SCENARIO.md (if exists)
  • Main Documentation: ../CLAUDE.md

Last Updated: November 5, 2025 Skills Deployed: 5/5 product skills production-ready Total Tools: 6 Python automation tools

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.