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Piper Morgan - AI Product Management Assistant

📋 Table of Contents

🎯 What is Piper Morgan?

Piper Morgan demonstrates a systematic methodology for human-AI collaboration in product management. Rather than replacing human judgment, it augments PM workflows through natural conversation, evolving from automating routine tasks to providing strategic insights.

💬 See It in Action

Before (Command Mode)

You: "Update GitHub issue #1247 status:done"
You: "Show me document requirements_v2.pdf"
You: "Assign issue #1247 to:sarah"

After (Conversational AI)

You: "Update that bug we discussed"
Piper: "✅ Updated issue #1247 (login timeout) status to done"

You: "Show me the latest requirements"
Piper: "📄 Here's requirements_v2.pdf (47 pages, updated 2 days ago)"

You: "Assign it to Sarah"
Piper: "✅ Assigned issue #1247 to Sarah. She's been notified."

Result: 5x faster workflows, 90% less mental overhead, conversations that feel human.

🚀 Quick Start (30 seconds)

# 1. Clone and setup
git clone https://github.com/mediajunkie/piper-morgan-product.git
cd piper-morgan-product
python -m venv venv && source venv/bin/activate  # or `venv\Scripts\activate` on Windows

# 2. Install dependencies
pip install -r requirements.txt

# 3. Configure environment
cp .env.example .env
# Add your API keys (OpenAI, Anthropic, GitHub)

# 4. Start infrastructure and launch
docker-compose up -d
python main.py

🎯 Choose Your Path

🚀 New to Piper? Start with our 15-minute getting started guide

👥 Team Lead or PM? See key capabilities and performance metrics

🔧 Developer or Architect? Jump to architecture documentation and developer resources

⚡ Ready to deploy? Try our one-click startup or web interface

🚀 One-Click Startup

For daily standup routine:

🖥️ CLI Commands

Issue Intelligence

Real-time GitHub issue analysis and intelligent prioritization:

# Get project health overview
python main.py issues status

# Intelligent issue triage and prioritization
python main.py issues triage --limit 10

# Discover patterns and cross-feature insights
python main.py issues patterns

# Morning standup with issue context
python main.py standup

Features:

📖 Full CLI Documentation 📊 Issue Intelligence Features

🌅 Morning Standup Web Interface

Launch your daily standup with a professional dark mode web interface - faster than CLI with comprehensive GitHub integration.

🚀 Quick Start

# Start FastAPI server
PYTHONPATH=. python web/app.py
# or
PYTHONPATH=. python -m uvicorn web.app:app --host 127.0.0.1 --port 8001

🌐 Access Points

⚡ Performance & Features

📊 What You Get

📖 Technical Documentation 📋 User Guide

📚 Complete Documentation

🎯 User Guides

🔧 Developer Resources

🏗️ Architecture & Design Documentation

Architecture Patterns - Implementation Patterns Library

30 proven patterns organized by functional category: Infrastructure & Architecture (001-010), Context & Session Management (011-017), Integration & Adapters (018-022), Query & Data Patterns (023-027), and AI & Orchestration (028-030). Each pattern follows ADR-style documentation with Context, Implementation, Usage Guidelines, and Examples in Codebase.

Architectural Decision Records - Decision History & Rationale

34 architectural decisions documenting the evolution from initial MCP integration through current multi-agent coordination. Organized by category: Foundation & Core Platform, Integration & Communication, Service Enhancement, Data & Repository Management, Infrastructure & Operations, Testing & Quality Assurance, Spatial Intelligence, and Methodological Architecture. Essential reading for understanding system architecture rationale.

🧪 Testing & Quality Assurance

⚡ Smart Test Infrastructure (Phase 1)

Our test infrastructure provides 4 execution modes optimized for development workflow:

Quick Test Commands:

# Smart test execution
./../scripts/run_tests.sh smoke     # <5s validation
./../scripts/run_tests.sh fast      # <30s development workflow
./../scripts/run_tests.sh full      # Complete test suite
./../scripts/run_tests.sh coverage  # Coverage analysis

# Git integration (automated)
git commit    # Runs smoke tests via pre-commit hook
git push      # Runs fast tests via pre-push hook

Excellence Flywheel Integration: All testing follows Verification First → Implementation → Evidence-based progress → GitHub tracking methodology.

See 🧪 Test Guide for complete documentation.

🚀 Recent Infrastructure Activations

🏗️ GREAT-3A: Plugin Architecture Foundation (October 2, 2025)

✅ GREAT-2 Epic Completion (September 30, 2025)

🔧 Multi-User Configuration System (September 6, 2025)

📚 Notion Integration (August 26, 2025)

🧪 Test Infrastructure (August 20, 2025)

🔄 Multi-Agent Coordination (August 20, 2025)

💾 Persistent Context Foundation (August 20, 2025)

📚 Enhanced Development Documentation

Core Methodology

🧭 Complete Methodology Index: methodology-core/INDEX.md - Full navigation guide ⚡ Quick Start: METHODOLOGY.md - Operational overview

Implementation Guides

Operations & Automation

📊 Roadmap Status

The Great Refactor Progress (~30% Complete)

Architecture Evolution

🎯 Current Capabilities (~80% Functional)

✅ Working Systems

🚧 In Development (GREAT-3B)

❌ Future Work

🏗️ Architecture Overview

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│   Conversation  │    │  Intent Service  │    │  Knowledge      │
│   Manager       │◄──►│  & Orchestration │◄──►│  Graph Service  │
│   (10-turn ctx) │    │  Engine          │    │  & Repositories │
└─────────────────┘    └──────────────────┘    └─────────────────┘
         │                       │                       │
         │                       │                       │
         ▼                       ▼                       ▼
┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│  Anaphoric      │    │  Integration     │    │  Learning       │
│  Reference      │    │  Services        │    │  (GitHub, Jira)  │
│  Resolution     │    │  (GitHub, Jira)  │    │  & Analytics    │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Core Services:

🎯 Key Features

Conversational AI Capabilities

User Experience Benefits

📊 Performance Metrics

Current System Performance (PM-034)

User Experience Metrics

🔧 Development

Internal Development Teams: For comprehensive internal documentation navigation, see NAVIGATION.md

Prerequisites

Local Development Setup

# Verify Python version (must be 3.11+)
python --version  # Should show Python 3.11.x

# Clone and setup
git clone https://github.com/mediajunkie/piper-morgan-product.git
cd piper-morgan-product

# Set up Python virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Copy environment template
cp .env.example .env
# Edit .env with your API keys and configuration

# Start infrastructure services
docker-compose up -d postgres redis

# Initialize the database
python scripts/init_db.py

# Start the development server
python main.py

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Workflow

  1. Fork the repository
  2. Create a feature branch (git checkout -b feature/amazing-feature)
  3. Commit your changes (git commit -m 'Add amazing feature')
  4. Push to the branch (git push origin feature/amazing-feature)
  5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE (coming soon) file for details.

🆘 Support

🎉 Ready to Get Started?

Choose your path:

🚀 New User? Start Here

🔄 Existing User? Upgrade Here

📖 Want Examples? See Scenarios

🔧 Technical Details? API Docs

Tests Code Coverage Documentation Python Version License: MIT

Made with ❤️ and Systematic Kindness by the Piper Morgan team