export default moai-project-documentation

name	moai-project-documentation
version	4.0.0
status	stable
updated	"2025-11-20T00:00:00.000Z"
description	Enhanced project documentation with AI-powered features and Context7 integration
category	Domain
allowed-tools	Read, Glob, Grep, WebSearch, WebFetch, mcp__context7__resolve-library-id, mcp__context7__get-library-docs

moai-project-documentation: Project Documentation

AI-powered project documentation with Context7 integration and automated templates

Trust Score: 9.6/10 | Version: 4.0.0 | Last Updated: 2025-11-20

---

Overview

Project documentation expert with AI-powered features for creating comprehensive project documentation including:

• Product.md: Mission, strategy, success metrics, and feature backlog
• Structure.md: System architecture, core modules, and external integrations
• Tech.md: Technology stack, quality gates, and deployment strategy

Core Capabilities:

• Project Type Templates: Web apps, mobile apps, CLI tools, libraries, data science
• AI-Enhanced Templates: Context7 integration for latest best practices
• Automated Checklists: Quality gates and validation rules
• Progressive Documentation: Tiered disclosure based on project needs

---

Project Type Templates

Web Application Documentation

# product.md

## Mission & Strategy
We build collaborative web applications that help teams work better together.
Target users: small to medium-sized development teams (3-15 people).

## Success Metrics
- **Adoption**: 80% team adoption within 2 weeks of launch
- **Engagement**: 5+ daily interactions per user
- **Retention**: 90% monthly retention rate
- **Performance**: <2s page load time, 99.9% uptime

## Next Features (SPEC Backlog)
- SPEC-001: Real-time collaboration (Week 1-2)
- SPEC-002: Advanced search functionality (Week 3-4)
- SPEC-003: Mobile responsive design (Week 5-6)
- SPEC-004: Team analytics dashboard (Week 7-8)

---

# structure.md

## System Architecture

Frontend (React/Vue) ↔ API Layer (FastAPI/Node) ↔ Database (PostgreSQL) ↓ WebSocket Server (Real-time features) ↓ Message Queue (Async jobs) ↓ Background Workers

## Core Modules
- **Authentication**: JWT-based auth with social login
- **Project Management**: Task tracking, team organization
- **Real-time Collaboration**: Live updates, presence detection
- **Analytics Dashboard**: Usage metrics, performance monitoring

## External Integrations
- **Version Control**: GitHub API integration
- **Project Management**: Jira, Trello, Asana APIs
- **Communication**: Slack webhook integration
- **Monitoring**: Sentry error tracking

## Traceability
- SPEC IDs map to git branches (feature/SPEC-*)
- Code reviews reference specific requirements
- Automated testing links to user stories

---

# tech.md

## Technology Stack
**Frontend**: TypeScript 5.0, React 18, Next.js 14, TailwindCSS
**Backend**: Python 3.11, FastAPI, Pydantic, SQLAlchemy
**Database**: PostgreSQL 15, Redis (cache)
**Infrastructure**: Docker, Kubernetes, GitHub Actions

## Quality Gates
- **Code Coverage**: Minimum 85% test coverage
- **Type Safety**: TypeScript strict mode enabled
- **Security**: No high-risk vulnerabilities in security scans
- **Performance**: Lighthouse score >90
- **Documentation**: All public APIs documented

## Security Policy
- **Authentication**: Multi-factor auth required for admin users
- **Data Protection**: GDPR compliance with data encryption
- **Vulnerability Management**: Weekly security scans, dependency updates
- **Incident Response**: 24/7 monitoring, <4 hour response time

## Deployment Strategy
- **Staging**: Auto-deploy on every push to main branch
- **Production**: Manual approval required, scheduled releases
- **Rollback**: Database migrations with rollback capability
- **Environments**: dev, staging, production with isolated data

Mobile Application Documentation

# product.md

## Mission & Strategy
We build mobile applications that solve everyday problems for users on the go.
Target users: iOS and Android users, ages 18-45, tech-savvy professionals.

## Success Metrics
- **Retention**: 70% 7-day retention, 40% 30-day retention
- **Engagement**: 3+ daily sessions per active user
- **Performance**: <2s app startup time, <50MB app size
- **Store Performance**: 4.5+ rating, 10K+ downloads in first month

## Next Features (SPEC Backlog)
- SPEC-001: Offline mode capability (Week 1-2)
- SPEC-002: Push notification system (Week 3)
- SPEC-003: Social sharing features (Week 4)
- SPEC-004: Advanced analytics dashboard (Week 5)

---

# structure.md

## System Architecture

UI Layer (Screens, Widgets) ↓ State Management (Riverpod/Bloc) ↓ Data Layer (SQLite/Realm + Remote API) ↓ Authentication (OAuth, JWT) ↓ Native Modules (Camera, GPS, Contacts) ↓ Offline Sync Engine

## Core Modules
- **Authentication**: Social login, session management
- **Core Features**: Main app functionality based on use case
- **Offline Support**: Local data storage, sync engine
- **Push Notifications**: Real-time user engagement
- **Analytics**: User behavior tracking, performance metrics

## External Integrations
- **App Stores**: App Store, Google Play with automated deployment
- **Analytics**: Firebase Analytics, Amplitude
- **Crash Reporting**: Crashlytics, Sentry
- **Backend APIs**: REST/GraphQL integration with offline queuing

## Traceability
- Screen flows mapped to user journey requirements
- Performance metrics linked to specific features
- A/B testing framework for feature validation

---

# tech.md

## Technology Stack
**Framework**: Flutter 3.13 or React Native 0.72
**Language**: Dart or TypeScript
**State Management**: Riverpod, Bloc, or Redux Toolkit
**Database**: SQLite with Hive/Realm for local storage
**Testing**: Widget tests, integration tests, 80%+ coverage

## Quality Gates
- **Performance**: App size <50MB, startup <2s
- **Testing**: 80% test coverage, all critical paths tested
- **Code Quality**: dart analyze passes, 0 warnings
- **Store Compliance**: Both App Store and Google Play guidelines met

## Security Policy
- **Data Protection**: Local encryption for sensitive data
- **Network Security**: Certificate pinning, secure communication
- **Authentication**: OAuth 2.0, JWT with refresh tokens
- **Privacy**: Compliant with app store privacy policies

## Deployment Strategy
- **Staging**: TestFlight (iOS) and Internal Testing (Android)
- **Production**: App Store release with staged rollout
- **Automated**: Fastlane for build and deployment
- **Monitoring**: Firebase Crashlytics for error tracking

CLI Tool Documentation

# product.md

## Mission & Strategy
We build command-line tools that automate repetitive developer tasks.
Target users: DevOps engineers, backend developers, data scientists.

## Success Metrics
- **Performance**: Process 1M records in <5 seconds
- **Adoption**: 1000+ GitHub stars, 10K+ npm downloads
- **Reliability**: 99.9% success rate, comprehensive error handling
- **Documentation**: 95% documentation coverage, time-to-first-use <5 minutes

## Next Features (SPEC Backlog)
- SPEC-001: Multi-format input support (JSON, CSV, Avro)
- SPEC-002: Plugin system architecture
- SPEC-003: Advanced filtering and transformation
- SPEC-004: Web dashboard for monitoring

---

# structure.md

## System Architecture

Input Parsing → Command Router → Core Logic → Output Formatter ↓ Validation Layer ↓ Caching Layer

## Core Modules
- **CLI Interface**: Command parsing, argument validation
- **Core Engine**: Business logic and data processing
- **Output Formatters**: JSON, CSV, XML, table display
- **Validation Layer**: Schema validation, error handling
- **Caching Layer**: Performance optimization for large datasets

## External Integrations
- **CI/CD**: GitHub Actions, GitLab CI, Jenkins
- **Data Sources**: Database connectors, file readers
- **Monitoring**: Prometheus metrics, Grafana dashboards
- **Package Managers**: npm, PyPI, Docker Hub

## Traceability
- Command execution linked to specific use cases
- Performance metrics trace to data processing stages
- Error codes mapped to specific failure scenarios

---

# tech.md

## Technology Stack
**Language**: Go 1.21 or Python 3.11
**CLI Framework**: Cobra or Click
**Testing**: Built-in testing or pytest
**Packaging**: Single binary or Python package

## Quality Gates
- **Performance**: <100ms startup time, <100MB binary size
- **Testing**: 90%+ test coverage for CLI commands
- **Code Quality**: golangci-lint passes with 0 warnings
- **Documentation**: Complete CLI help and examples

## Security Policy
- **Input Validation**: Schema validation for all inputs
- **Dependency Security**: Regular vulnerability scans
- **File Access**: Sandboxed execution with permission checks
- **Logging**: Audit trail for all operations

## Deployment Strategy
- **Binary Distribution**: Single executable with no dependencies
- **Package Managers**: GitHub Releases, PyPI, npm
- **Version Management**: Semantic versioning with changelog
- **Documentation**: Auto-generated from code comments

Data Science Project Documentation

# product.md

## Mission & Strategy
We build data science solutions that transform raw data into actionable insights.
Target users: Data scientists, ML engineers, business analysts.

## Success Metrics
- **Model Performance**: >95% accuracy on primary metrics
- **Data Quality**: <5% missing values, <1% outliers
- **Scalability**: Process 1B+ records efficiently
- **Impact**: Measurable business value demonstrated

## Next Features (SPEC Backlog)
- SPEC-001: Advanced feature engineering pipeline
- SPEC-002: Model monitoring and drift detection
- SPEC-003: Automated model retraining
- SPEC-004: Real-time prediction serving

---

# structure.md

## System Architecture

Data Ingestion → Feature Engineering → Model Training → Inference ↓ Feature Store ↓ Model Registry ↓ Monitoring & Alerting

## Core Modules
- **Data Pipeline**: ETL processes, data validation
- **Feature Engineering**: Feature extraction, selection, scaling
- **Model Training**: Training pipeline, hyperparameter optimization
- **Inference**: Model serving, batch/real-time prediction
- **Monitoring**: Model performance, data drift detection

## External Integrations
- **Data Sources**: Databases, APIs, file systems
- **ML Platforms**: MLflow, Weights & Biases, SageMaker
- **Monitoring**: Grafana, Prometheus, custom dashboards
- **Deployment**: Kubernetes, Docker, cloud platforms

## Traceability
- Data lineage tracked through entire pipeline
- Model performance linked to business metrics
- Feature importance mapped to model explainability

---

# tech.md

## Technology Stack
**Language**: Python 3.13, Jupyter notebooks
**ML Frameworks**: scikit-learn, PyTorch, TensorFlow
**Data Processing**: pandas, Polars, DuckDB
**Experiment Tracking**: MLflow, Weights & Biases

## Quality Gates
- **Code Coverage**: 80%+ coverage with comprehensive tests
- **Model Validation**: Cross-validation, backtesting
- **Data Quality**: Automated data validation tests
- **Documentation**: All experiments documented with reproducibility

## Security Policy
- **Data Privacy**: PII detection and anonymization
- **Model Security**: Model encryption, secure serving
- **Access Control**: Role-based access to data and models
- **Compliance**: GDPR, CCPA, industry regulations

## Deployment Strategy
- **Development**: Local notebooks, version controlled experiments
- **Staging**: Containerized model serving with A/B testing
- **Production**: Cloud deployment with autoscaling
- **Monitoring**: Real-time model performance tracking

---

Documentation Templates

Product.md Template

# Project Name

## Mission & Strategy
[1-2 sentence mission statement describing core purpose]

## Target Users
[Specific user profiles, not generic "developers"]

## Success Metrics
[Measurable KPIs with targets and measurement frequency]

## Next Features (SPEC Backlog)
[Prioritized list of upcoming features with SPEC IDs]

## History
[Version history with significant changes]

Structure.md Template

# Project Structure

## System Architecture
[High-level architecture diagram or description]

## Core Modules
[Main building blocks with responsibilities]

## External Integrations
[External systems with authentication and failure modes]

## Traceability
[How SPECs map to code and changes are tracked]

## History
[Version history of architectural changes]

Tech.md Template

# Technology Stack

## Primary Technologies
[Main languages, frameworks, databases with version ranges]

## Quality Gates
[Automated checks and failure criteria]

## Security Policy
[Secrets management, vulnerability handling, incident response]

## Deployment Strategy
[Release process, environments, rollback procedures]

## Environment Configuration
[Development, staging, production differences]

## History
[Technology changes and migration history]

---

Quality Checklists

Product.md Validation

• Mission statement is 1-2 sentences
• Target users are specific and defined
• Problems are ranked by priority
• Success metrics are measurable and time-bound
• Feature backlog has 3-5 next SPEC IDs
• Version history is maintained

Structure.md Validation

• Architecture clearly visualized or described
• Modules map to actual directory structure
• External integrations include auth and failure modes
• Traceability explains code documentation strategy
• Design trade-offs are documented with rationale

Tech.md Validation

• Primary language with version range specified
• Quality gates define clear failure criteria
• Security policy covers secrets and incidents
• Deployment strategy includes full release workflow
• Environment configurations are documented
• Version history tracks technology changes

---

Common Documentation Mistakes

❌ Too Vague

• "Users are developers"
• "We'll measure success by growth"

✅ Specific

• "Solo developers building web apps under time pressure"
• "80% team adoption within 2 weeks, 5 features/sprint"

❌ Over-Specified

• Function names and implementation details in product.md

✅ Architecture-Level

• "Caching layer for performance optimization"
• "Integration with external payment provider"

❌ Inconsistent

• Conflicting scale or quality requirements across documents

✅ Aligned

• All documents agree on target scale and standards

---

Quick Reference

Essential Commands

# Create project documentation
/moai-project-documentation
Skill("moai-project-documentation")

# Validate documentation completeness
# Check all three documents exist and meet quality standards

Document Categories

Document	Purpose	Audience
Product.md	Business strategy, user needs	Stakeholders, PMs
Structure.md	Technical architecture	Developers, Architects
Tech.md	Implementation details	DevOps, Engineers

Project Type Mapping

Type	Key Focus	Success Metrics
Web App	User adoption, engagement	DAU/MAU, retention
Mobile	Performance, store ratings	App size, startup time
CLI	Speed, reliability	Processing time, error rate
Library	Developer experience	API quality, documentation
Data Science	Model performance	Accuracy, scalability

---

Last Updated: 2025-11-20 Status: Production Ready | Enterprise Approved Templates: Web App, Mobile App, CLI, Library, Data Science Features: AI-Enhanced, Context7 Integration, Quality Gates