Skip to content

Template Architecture Design

Document Type: Architecture Design Document Version: 1.0.0 Date: 2025-10-17 Status: Design Phase

Executive Summary

This document defines the optimal structure for two production-ready templates that enable users to kickstart projects with AI assistance:

  1. KICKSTART_REPO.md - Template users copy to their repository root
  2. PROJECT_PLAN_TEMPLATE.md - Template LLMs use to generate comprehensive project documentation

Design Philosophy: Progressive disclosure, LLM-friendly, works standalone, self-documenting, production-ready.

Template 1: KICKSTART_REPO.md

Purpose

A standalone, fill-in-the-blank template that users copy to their new repository root. It works without requiring the standards repository to be present and guides LLMs to:

  1. Auto-detect the tech stack from repository contents
  2. Generate a comprehensive PROJECT_PLAN.md
  3. Integrate with @load directives if available
  4. Provide smart defaults for common scenarios

Content Architecture

Section 1: Header & Quick Start (Lines 1-50)

Purpose: Immediate orientation and 30-second start path

# 🚀 Project Kickstart

**Auto-generate project structure, standards, and documentation using AI.**

## Quick Start (30 seconds)

1. Fill in basic details below (or leave blank for auto-detection)
2. Copy this entire file to your AI assistant (Claude, ChatGPT, Gemini)
3. AI will analyze your repo and generate PROJECT_PLAN.md

---

Rationale:

  • Clear value proposition immediately
  • Minimal friction to start
  • Works even if user doesn't fill anything in

Section 2: Project Basics (Lines 51-120)

Purpose: Essential project information with smart defaults

## Project Information

### Basic Details

**Project Name**: [Auto-detect from repo name OR enter manually]
**Project Type**: [Auto-detect OR select: API / Web App / Mobile / CLI / Data Pipeline / ML Service]
**Primary Language**: [Auto-detect from files OR enter: Python / TypeScript / Go / Java / etc.]
**Timeline**: [Enter: MVP / 3-month / 6-month OR leave blank]

### Tech Stack (Optional - AI will auto-detect)

**Frontend**: [e.g., React, Vue, Angular, or N/A]
**Backend**: [e.g., FastAPI, Express, Django, or N/A]
**Database**: [e.g., PostgreSQL, MongoDB, MySQL, or N/A]
**Infrastructure**: [e.g., Docker, Kubernetes, AWS, or N/A]

### Team Context (Optional)

**Team Size**: [Solo / Small (2-5) / Medium (6-15) / Large (15+)]
**Experience Level**: [Beginner / Intermediate / Expert]
**Compliance Needs**: [None / SOC2 / HIPAA / PCI-DSS / GDPR]

Rationale:

  • Bracketed placeholders guide without overwhelming
  • "Auto-detect" reduces friction
  • Optional sections allow quick start
  • Compliance captured early for security integration

Section 3: AI Instructions (Lines 121-250)

Purpose: Direct the LLM's analysis and generation process

---

## AI Assistant Instructions

You are analyzing this repository to generate a comprehensive project plan. Follow these steps:

### Step 1: Repository Analysis

Scan the repository and detect:

```yaml
tech_stack:
  languages: []        # From file extensions
  frameworks: []       # From package.json, requirements.txt, go.mod, etc.
  databases: []        # From config files, docker-compose.yml
  infrastructure: []   # From Dockerfiles, k8s manifests, terraform

project_type: ""      # API / Web App / Mobile / CLI / Data Pipeline / ML Service
primary_language: ""  # Most prominent language
confidence: 0.0       # 0.0-1.0 confidence in detection

Detection Rules:

  • If package.json + src/components/ → React/Vue/Angular frontend
  • If requirements.txt + app/main.py → Python backend
  • If Dockerfile present → Containerized deployment
  • If .github/workflows/ → CI/CD already configured
  • If tests/ directory → Testing already considered

Step 2: Standards Recommendation

Based on detected tech stack, recommend standards from: Standards Repository: https://github.com/williamzujkowski/standards

Use Product Matrix Syntax:

@load product:[type]              # e.g., @load product:api
@load [product:xxx + CS:lang]     # e.g., @load [product:api + CS:python]
@load [CS:lang + TS:* + SEC:*]    # All testing and security standards

Standard Code Reference:

  • CS = Coding Standards (language-specific)
  • TS = Testing Standards (framework-specific)
  • SEC = Security Standards (auth, secrets, encryption)
  • FE = Frontend Standards (React, Vue, mobile)
  • DOP = DevOps Standards (CI/CD, IaC)
  • DE = Data Engineering (pipelines, quality)
  • NIST-IG = NIST 800-53r5 compliance (auto-included with SEC)

Examples:

  • Python API: @load [product:api + CS:python + TS:pytest + SEC:*]
  • React App: @load [product:frontend-web + FE:react + SEC:auth]
  • Data Pipeline: @load [product:data-pipeline + DE:* + OBS:monitoring]

Step 3: Generate PROJECT_PLAN.md

Create a comprehensive project plan using the template at: https://github.com/williamzujkowski/standards/blob/master/templates/PROJECT_PLAN_TEMPLATE.md

Required Sections:

  1. Project Overview (with detected tech stack)
  2. Standards Alignment (with @load directives)
  3. Architecture & Structure (directory tree)
  4. Implementation Phases (timeline with milestones)
  5. Quality Gates (testing, security, compliance)
  6. Quick Start Commands (setup, run, test, deploy)

Step 4: Configuration Generation

Generate starter files based on detected stack:

  • README.md with setup instructions
  • Language-specific config (package.json, pyproject.toml, go.mod)
  • Testing config (.jest.config.js, pytest.ini, etc.)
  • CI/CD pipeline (.github/workflows/ci.yml)
  • Docker setup (Dockerfile, docker-compose.yml)
  • Environment template (.env.example)

Step 5: Validation Checklist

Ensure generated plan includes:

  • ✅ All required sections complete
  • ✅ Tech stack matches repository contents
  • ✅ Standards codes are valid
  • ✅ Quick start commands are executable
  • ✅ Directory structure is sensible
  • ✅ Security measures included
  • ✅ Testing strategy defined
  • ✅ CI/CD pipeline included
**Rationale**:
- Explicit step-by-step process reduces ambiguity
- Code blocks make instructions clear
- Examples prevent confusion
- Validation ensures quality output
- Links to standards repo for reference

#### Section 4: Fallback Mode (Lines 251-320)

**Purpose**: Handle cases where repository is empty or AI can't access it

```markdown
---

## Fallback: Manual Project Description

**If repository is empty or AI cannot analyze files, use this section:**

### What I'm Building

[Describe your project in 2-3 sentences. What problem does it solve? Who uses it?]

### Core Features

1. [Feature 1 - e.g., "User authentication with OAuth2"]
2. [Feature 2 - e.g., "RESTful API for data access"]
3. [Feature 3 - e.g., "Real-time notifications via WebSocket"]

### Technical Requirements

**Must Have:**
- [Requirement 1 - e.g., "Deploy to AWS Lambda"]
- [Requirement 2 - e.g., "Support 1000 concurrent users"]
- [Requirement 3 - e.g., "HIPAA compliant data handling"]

**Nice to Have:**
- [Optional feature 1]
- [Optional feature 2]

### Known Constraints

- [Constraint 1 - e.g., "Must integrate with legacy SOAP API"]
- [Constraint 2 - e.g., "Budget: $500/month for infrastructure"]

Rationale:

  • Provides escape hatch for edge cases
  • Keeps users from getting stuck
  • Simple structure lowers barrier

Section 5: Integration Hooks (Lines 321-380)

Purpose: Connect with standards repository features if available

---

## Advanced: Standards Repository Integration

**If you have the standards repository available:**

### Skills System (98% Token Reduction)

Instead of loading full documents, use progressive skills:

```bash
# Recommend skills based on this repo
python3 /path/to/standards/scripts/skill-loader.py recommend ./

# Load recommended skills
@load product:api --level 1

# Token comparison:
# - All standards: ~150,000 tokens
# - Skills Level 1: ~1,755 tokens (98.8% reduction)

Router Integration

If using Claude Code with CLAUDE.md router:

# Fast path syntax
@load product:api
@load product:frontend-web
@load [product:api + CS:python + TS:pytest]

Manual Standards Loading

# Browse available standards
https://github.com/williamzujkowski/standards/blob/master/docs/standards/UNIFIED_STANDARDS.md

# Copy relevant sections to your project
https://github.com/williamzujkowski/standards/tree/master/examples/project-templates

Output Format

Expected AI Response:

  1. Tech Stack Analysis (YAML format)
  2. Standards Recommendations (@load directives)
  3. PROJECT_PLAN.md (complete, ready to save)
  4. Starter Files (README, configs, CI/CD)
  5. Quick Start Commands (bash scripts)

Save the generated files and start building!

**Rationale**:
- Makes advanced features discoverable
- Provides token efficiency information
- Shows integration points without requiring them
- Clear expectations for output

### Key Design Decisions

#### Decision 1: Standalone Operation

**Rationale**: Users may not have the standards repository cloned locally. The template must:
- Work in isolation
- Embed essential context
- Provide fallback URLs to standards
- Not break if standards repo is unavailable

**Implementation**: All critical information embedded; external links are supplementary.

#### Decision 2: Progressive Disclosure

**Rationale**: Users have different needs and time constraints.

**Implementation**:
- Quick Start section for 30-second usage
- Optional sections for detailed configuration
- Advanced section for power users
- Fallback section for edge cases

#### Decision 3: Dual-Mode Operation

**Modes**:
1. **Auto-Detection Mode**: AI scans repository files
2. **Manual Mode**: User fills in project description

**Rationale**: Handles both "empty new project" and "analyze existing code" scenarios.

#### Decision 4: LLM-Agnostic Language

**Rationale**: Works with Claude, ChatGPT, Gemini, Cursor, and future LLMs.

**Implementation**:
- Clear natural language instructions
- No tool-specific syntax (except optional sections)
- Standard markdown formatting
- Explicit examples for all concepts

### Usage Flow Examples

#### Example 1: New Python API Project

User Actions:

  1. Creates new empty repo
  2. Copies KICKSTART_REPO.md to repo root
  3. Fills in: Project Name: "Task Manager API", Project Type: "API"
  4. Pastes entire file to Claude

Claude Response:

  • Detects: Empty repo, user wants Python API
  • Recommends: @load [product:api + CS:python + TS:pytest + SEC:*]
  • Generates: PROJECT_PLAN.md with FastAPI structure
  • Creates: pyproject.toml, pytest.ini, Dockerfile, .github/workflows/ci.yml
  • Provides: Setup commands (poetry install, pytest, docker build)

User Gets:

  • Complete project structure
  • Standards-aligned configuration
  • Ready to code in 5 minutes
#### Example 2: Existing React Project

User Actions:

  1. Has existing React app with some code
  2. Copies KICKSTART_REPO.md to repo root
  3. Leaves all fields blank (triggers auto-detection)
  4. Pastes entire file to ChatGPT

ChatGPT Response:

  • Scans: package.json, src/components/, detects React + TypeScript
  • Recommends: @load [product:frontend-web + FE:react + TS:vitest]
  • Generates: PROJECT_PLAN.md documenting current state + improvements
  • Suggests: Missing tests, CI/CD, accessibility checks
  • Creates: Updated configs, GitHub Actions, testing setup

User Gets:

  • Documentation of existing project
  • Gaps identified and filled
  • Production-ready CI/CD
---

## Template 2: PROJECT_PLAN_TEMPLATE.md

### Purpose

A **comprehensive markdown template** that LLMs use to generate complete project documentation. It serves as:

1. The target format for AI-generated plans
2. A reference for manual project planning
3. A living document updated throughout development
4. A source of truth for team alignment

### Content Architecture

#### Section 1: Document Header (Lines 1-30)

**Purpose**: Metadata and version tracking

```markdown
# Project Plan: [PROJECT_NAME]

**Document Version**: 1.0.0
**Last Updated**: [YYYY-MM-DD]
**Status**: [Planning / In Progress / Production]
**Owner**: [Team/Individual Name]

---

## Document Purpose

This project plan serves as the single source of truth for:
- Technical architecture and implementation details
- Standards alignment and quality gates
- Timeline and milestone tracking
- Team responsibilities and deliverables

**Generated By**: AI Assistant (Claude/ChatGPT/Gemini)
**Based On**: [KICKSTART_REPO.md OR Manual Input]
**Standards Repository**: https://github.com/williamzujkowski/standards

---

## Quick Navigation

- [Project Overview](#project-overview)
- [Tech Stack](#tech-stack-analysis)
- [Standards Alignment](#standards-alignment)
- [Architecture](#system-architecture)
- [Implementation Timeline](#implementation-phases)
- [Quality Gates](#quality-gates)
- [Security](#security-implementation)
- [Testing](#testing-strategy)
- [Deployment](#deployment-strategy)
- [Team](#team-roles)

---

Rationale:

  • Establishes document as living artifact
  • Quick navigation for large documents
  • Metadata enables version tracking
  • Clear purpose prevents scope creep

Section 2: Project Overview (Lines 31-100)

Purpose: High-level project description and context

## Project Overview

### Executive Summary

[2-3 paragraph description of the project]

**Problem Statement**: [What problem are we solving?]

**Solution Approach**: [How are we solving it?]

**Success Criteria**: [How do we measure success?]

### Project Metadata

| Attribute | Value |
|-----------|-------|
| **Project Type** | [API / Web App / Mobile / CLI / Data Pipeline / ML Service] |
| **Primary Language** | [Python / TypeScript / Go / Java / etc.] |
| **Deployment Target** | [AWS / GCP / Azure / On-Premise / Hybrid] |
| **Team Size** | [Solo / 2-5 / 6-15 / 15+] |
| **Timeline** | [MVP: X weeks, Production: Y weeks] |
| **Budget** | [Development: $X, Infrastructure: $Y/month] |

### Stakeholders

| Role | Name/Team | Responsibilities |
|------|-----------|------------------|
| Product Owner | [Name] | Feature prioritization, requirements |
| Tech Lead | [Name] | Architecture, technical decisions |
| Dev Team | [Names] | Implementation, testing |
| Security Lead | [Name] | Security review, compliance |
| DevOps | [Name/Team] | Infrastructure, CI/CD |

### Project Context

**Business Drivers**:
- [Driver 1 - e.g., "Reduce customer onboarding time by 50%"]
- [Driver 2]
- [Driver 3]

**Key Assumptions**:
1. [Assumption 1 - e.g., "Users have modern browsers (Chrome 90+)"]
2. [Assumption 2]

**Known Constraints**:
1. [Constraint 1 - e.g., "Must integrate with legacy LDAP system"]
2. [Constraint 2]

**Out of Scope** (Explicitly NOT included):
- [Item 1]
- [Item 2]

Rationale:

  • Table format makes metadata scannable
  • Stakeholders section clarifies roles early
  • Context section prevents assumption gaps
  • Out of scope prevents feature creep

Section 3: Tech Stack Analysis (Lines 101-180)

Purpose: Document detected/chosen technology stack with rationale

## Tech Stack Analysis

### Detection Summary

**Detection Method**: [Auto-detected from repository / Manually specified / Hybrid]
**Confidence Level**: [High (90%+) / Medium (70-89%) / Low (<70%)]
**Analysis Date**: [YYYY-MM-DD]

### Technology Breakdown

#### Primary Languages

| Language | Usage | Version | Rationale |
|----------|-------|---------|-----------|
| Python | Backend API | 3.11+ | Type hints, async support, ecosystem |
| TypeScript | Frontend | 5.0+ | Type safety, tooling support |

#### Frameworks & Libraries

**Backend:**
- **FastAPI** (v0.104+) - Modern async Python framework
  - Rationale: Auto OpenAPI docs, Pydantic validation, async/await
  - Alternatives considered: Django REST, Flask

**Frontend:**
- **React** (v18+) with **Vite**
  - Rationale: Component model, ecosystem, performance
  - Alternatives considered: Vue, Svelte

**Testing:**
- **pytest** (Backend) - Fixtures, parametrization, plugins
- **Vitest** (Frontend) - Vite-native, fast, compatible with Jest

#### Data Layer

| Component | Technology | Version | Purpose |
|-----------|-----------|---------|---------|
| Primary Database | PostgreSQL | 15+ | Relational data, ACID compliance |
| Cache | Redis | 7+ | Session storage, rate limiting |
| Message Queue | RabbitMQ | 3.12+ | Async task processing |

**Schema Management**: Alembic (migrations), SQLAlchemy (ORM)

#### Infrastructure

**Containerization:**
- Docker (multi-stage builds)
- Docker Compose (local development)
- Kubernetes (production)

**Cloud Provider**: AWS
- **Compute**: ECS Fargate (backend), S3 + CloudFront (frontend)
- **Database**: RDS PostgreSQL with read replicas
- **Networking**: VPC, ALB, Route53
- **Security**: Secrets Manager, WAF, GuardDuty

**CI/CD:**
- GitHub Actions (build, test, deploy)
- AWS CodeDeploy (blue/green deployments)

#### Development Tools

| Category | Tool | Purpose |
|----------|------|---------|
| Linting | Ruff (Python), ESLint (TS) | Code quality |
| Formatting | Black (Python), Prettier (TS) | Consistent style |
| Type Checking | mypy, TypeScript compiler | Static analysis |
| Pre-commit | pre-commit framework | Git hooks |

### Tech Stack Diagram

┌─────────────────────────────────────────────────┐ │ Clients │ │ (Web Browser, Mobile App) │ └────────────────┬────────────────────────────────┘ │ ▼ ┌─────────────────────────────────────────────────┐ │ CloudFront CDN + WAF │ └────────────────┬────────────────────────────────┘ │ ┌─────────┴─────────┐ ▼ ▼ ┌─────────────┐ ┌──────────────────┐ │ S3 Bucket │ │ Application │ │ (Static) │ │ Load Balancer │ └─────────────┘ └────────┬─────────┘ │ ┌────────┴────────┐ ▼ ▼ ┌──────────────┐ ┌──────────────┐ │ ECS Fargate │ │ ECS Fargate │ │ (API - 1) │ │ (API - 2) │ └──────┬───────┘ └──────┬───────┘ │ │ └────────┬────────┘ │ ┌────────────┼────────────┐ ▼ ▼ ▼ ┌──────────┐ ┌──────────┐ ┌──────────┐ │ RDS │ │ Redis │ │ RabbitMQ │ │PostgreSQL│ │ Cache │ │ Queue │ └──────────┘ └──────────┘ └──────────┘

Rationale:

  • Tables make comparisons easy
  • Rationale for each choice prevents "why did we choose this?" questions
  • Alternatives considered shows thoughtful decision-making
  • Diagram provides visual understanding
  • Version constraints prevent compatibility issues

Section 4: Standards Alignment (Lines 181-280)

Purpose: Map project to relevant standards from the repository

## Standards Alignment

### Standards Loading Directive

**Auto-Load Command**:

@load [product:api + CS:python + CS:typescript + TS:pytest + TS:vitest + SEC:* + NIST-IG:base]

**Token Efficiency**:
- Using Skills System Level 1: ~2,500 tokens
- Traditional full standards: ~150,000 tokens
- **Reduction**: 98.3%

### Applied Standards Matrix

| Standard Code | Document | Scope | Priority | Implementation Status |
|--------------|----------|-------|----------|---------------------|
| CS:python | Coding Standards (Python) | Backend code quality | High | ✅ Configured (Ruff, Black) |
| CS:typescript | Coding Standards (TypeScript) | Frontend code quality | High | ✅ Configured (ESLint, Prettier) |
| TS:pytest | Testing Standards (pytest) | Backend testing | High | 🟡 In Progress (70% coverage) |
| TS:vitest | Testing Standards (Vitest) | Frontend testing | High | 🟡 In Progress (60% coverage) |
| SEC:auth | Authentication Standards | User authentication | Critical | ⚪ Planned (OAuth2 + JWT) |
| SEC:secrets | Secrets Management | API keys, credentials | Critical | ✅ Configured (AWS Secrets Manager) |
| SEC:input-validation | Input Validation | API security | Critical | ✅ Configured (Pydantic) |
| SEC:encryption | Encryption Standards | Data protection | High | ⚪ Planned (TLS, at-rest encryption) |
| NIST-IG:base | NIST Baseline Controls | Compliance | High | 🟡 In Progress (40% tagged) |
| DOP:ci-cd | CI/CD Standards | Deployment automation | High | ✅ Configured (GitHub Actions) |
| OBS:monitoring | Observability | Production monitoring | Medium | ⚪ Planned (CloudWatch, Sentry) |

**Legend**: ✅ Complete | 🟡 In Progress | ⚪ Planned | ❌ Blocked

### NIST 800-53r5 Control Mapping

**Applicable Controls** (auto-included with SEC standards):

| Control ID | Control Name | Implementation | Evidence |
|-----------|--------------|----------------|----------|
| AC-2 | Account Management | User CRUD with role-based access | `src/auth/users.py` |
| IA-2 | Identification & Authentication | OAuth2 + JWT tokens | `src/auth/oauth.py` |
| AU-2 | Audit Events | Structured logging of security events | `src/middleware/audit.py` |
| SC-8 | Transmission Confidentiality | TLS 1.3 for all traffic | `infrastructure/alb.tf` |
| SC-28 | Protection of Information at Rest | Database encryption enabled | `infrastructure/rds.tf` |

**Control Tagging Example**:
```python
# @nist-controls AC-2, IA-2
async def create_user(user_data: UserCreate) -> User:
    """
    Create new user with role assignment.

    NIST Controls:
    - AC-2: Account creation with automated notifications
    - IA-2: Password requirements enforced via validation
    """
    # Implementation...

Standards Compliance Checklist

Coding Standards:

  • ✅ Linter configured and passing (Ruff, ESLint)
  • ✅ Formatter configured and passing (Black, Prettier)
  • ✅ Type checking enabled (mypy, tsc)
  • ✅ Pre-commit hooks installed
  • ⚪ Code review process documented

Testing Standards:

  • ✅ Test framework configured (pytest, Vitest)
  • 🟡 Coverage >= 80% (currently 65%)
  • ⚪ Integration tests defined
  • ⚪ E2E tests implemented
  • ⚪ Performance tests defined

Security Standards:

  • ✅ Secrets management configured
  • ⚪ Authentication implemented
  • ⚪ Authorization implemented
  • ⚪ Input validation on all endpoints
  • ⚪ Security scanning in CI/CD
  • ⚪ Dependency vulnerability scanning

Documentation Standards:

  • ✅ README with setup instructions
  • ⚪ API documentation (OpenAPI/Swagger)
  • ⚪ Architecture diagrams
  • ⚪ Deployment runbooks
  • ⚪ Incident response procedures
**Rationale**:
- `@load` directive shows standards integration
- Token efficiency quantified
- Matrix provides clear status tracking
- NIST mapping ensures compliance
- Control tagging shows implementation
- Checklist makes gaps visible

#### Section 5: System Architecture (Lines 281-400)

**Purpose**: Define system design, components, and interactions

```markdown
## System Architecture

### Architecture Style

**Pattern**: Microservices with API Gateway
**Rationale**: Independent scaling, technology diversity, fault isolation

**Alternative Patterns Considered**:
- Monolith: Rejected (scaling limitations, deployment coupling)
- Serverless: Considered for future migration

### Component Architecture

┌──────────────────────────────────────────────────────┐ │ Client Layer │ │ (Web Browser, Mobile App, Third-party Integrations) │ └─────────────────────┬────────────────────────────────┘ │ ▼ ┌──────────────────────────────────────────────────────┐ │ API Gateway │ │ - Request routing │ │ - Rate limiting (100 req/min/user) │ │ - Authentication (JWT validation) │ │ - Request/response transformation │ └─────────────────────┬────────────────────────────────┘ │ ┌─────────────┼─────────────┐ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ Auth Service │ │ Core API │ │ Notification │ │ │ │ │ │ Service │ │ - Login │ │ - CRUD Ops │ │ - Email │ │ - Register │ │ - Business │ │ - SMS │ │ - Token Mgmt │ │ Logic │ │ - Push │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ └────────────────┼────────────────┘ │ ┌───────────────┼───────────────┐ ▼ ▼ ▼ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ PostgreSQL │ │ Redis │ │ RabbitMQ │ │ (Primary) │ │ (Cache + │ │ (Async │ │ │ │ Sessions) │ │ Tasks) │ └──────────────┘ └──────────────┘ └──────────────┘

### Service Breakdown

#### Auth Service

**Responsibilities**:
- User registration and login
- OAuth2 authorization code flow
- JWT token generation and validation
- Password reset workflows

**API Endpoints**:

POST /auth/register POST /auth/login POST /auth/refresh POST /auth/logout GET /auth/me POST /auth/password-reset/request POST /auth/password-reset/confirm

**Dependencies**:
- PostgreSQL (user storage)
- Redis (session management, token blacklist)
- Email service (password reset, verification)

**Technology**: FastAPI, SQLAlchemy, python-jose (JWT)

#### Core API Service

**Responsibilities**:
- Business entity CRUD operations
- Domain logic execution
- Data validation and transformation

**API Endpoints**:

GET /api/v1/resources POST /api/v1/resources GET /api/v1/resources/{id} PUT /api/v1/resources/{id} DELETE /api/v1/resources/{id}

**Dependencies**:
- PostgreSQL (data persistence)
- Redis (caching frequently accessed data)
- RabbitMQ (async task delegation)

**Technology**: FastAPI, SQLAlchemy, Pydantic

### Data Architecture

#### Database Schema

**Users Table**:
```sql
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    hashed_password TEXT NOT NULL,
    full_name VARCHAR(255),
    is_active BOOLEAN DEFAULT true,
    is_verified BOOLEAN DEFAULT false,
    role VARCHAR(50) DEFAULT 'user',
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_role ON users(role);

Resources Table (example):

CREATE TABLE resources (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    name VARCHAR(255) NOT NULL,
    description TEXT,
    owner_id UUID REFERENCES users(id) ON DELETE CASCADE,
    status VARCHAR(50) DEFAULT 'active',
    metadata JSONB,
    created_at TIMESTAMP DEFAULT NOW(),
    updated_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX idx_resources_owner ON resources(owner_id);
CREATE INDEX idx_resources_status ON resources(status);
CREATE INDEX idx_resources_metadata ON resources USING GIN (metadata);

Data Flow Diagram

Write Path:

Client → API Gateway → Core API → Validation → Database → Cache Invalidation → Response

Read Path (cached):

Client → API Gateway → Core API → Cache Check (Hit) → Response

Read Path (cache miss):

Client → API Gateway → Core API → Cache Check (Miss) → Database → Cache Update → Response

Security Architecture

Defense in Depth Layers:

  1. Network Layer: VPC isolation, security groups, NACLs
  2. Application Layer: WAF rules, rate limiting, input validation
  3. Authentication Layer: OAuth2 + JWT, MFA optional
  4. Authorization Layer: RBAC with resource-level permissions
  5. Data Layer: Encryption at rest and in transit, field-level encryption for PII

Threat Model (STRIDE):

  • Spoofing: JWT signature validation, OAuth2 flows
  • Tampering: Input validation, HMAC for critical data
  • Repudiation: Audit logging of all actions
  • Information Disclosure: TLS everywhere, encrypted DB fields
  • Denial of Service: Rate limiting, autoscaling, WAF
  • Elevation of Privilege: RBAC, least privilege principle

Scalability Design

Horizontal Scaling:

  • Stateless services (scale containers independently)
  • Session data in Redis (shared state)
  • Database read replicas (read-heavy workloads)

Vertical Scaling Limits:

  • Services: 4GB memory, 2 vCPU (measured baseline)
  • Database: RDS PostgreSQL (db.t4g.large initially, upgrade path to db.r6g.2xlarge)

Caching Strategy:

  • L1: In-memory service cache (LRU, 100MB limit)
  • L2: Redis cache (TTL: 5 minutes for reads, invalidate on writes)

Performance Targets: | Metric | Target | Monitoring | |--------|--------|------------| | API Response (p95) | < 200ms | CloudWatch | | API Response (p99) | < 500ms | CloudWatch | | Database Query (p95) | < 50ms | RDS Performance Insights | | Cache Hit Rate | > 80% | Redis INFO stats |

**Rationale**:
- Diagrams provide visual understanding
- Service breakdown clarifies responsibilities
- Database schemas are concrete, reviewable
- Security architecture addresses STRIDE threats
- Scalability design shows future-proofing

#### Section 6: Implementation Phases (Lines 401-550)

**Purpose**: Break down implementation into manageable phases with clear milestones

```markdown
## Implementation Phases

### Timeline Overview

**Total Duration**: 12 weeks (MVP in 8 weeks, production hardening in 4 weeks)

| Phase | Duration | Milestone | Success Criteria |
|-------|----------|-----------|-----------------|
| Phase 1: Foundation | Weeks 1-2 | Infrastructure + Basic API | Deployable hello-world service |
| Phase 2: Core Features | Weeks 3-6 | Authentication + CRUD | User can register, login, manage resources |
| Phase 3: Hardening | Weeks 7-8 | Testing + Security | 80% test coverage, security review passed |
| Phase 4: Production | Weeks 9-12 | Monitoring + Docs | Production deployment, runbooks complete |

---

### Phase 1: Foundation (Weeks 1-2)

**Goal**: Deployable infrastructure and hello-world API

**Week 1: Infrastructure Setup**

**Days 1-2: Development Environment**
- [ ] Initialize Git repository with .gitignore
- [ ] Set up project structure (src/, tests/, docs/, infrastructure/)
- [ ] Configure Python environment (pyproject.toml, .python-version)
- [ ] Configure Node environment (package.json, .nvmrc)
- [ ] Install development tools (Ruff, Black, ESLint, Prettier)
- [ ] Set up pre-commit hooks
- [ ] Create README with setup instructions

**Days 3-4: Infrastructure as Code**
- [ ] Define VPC, subnets, security groups (Terraform)
- [ ] Set up RDS PostgreSQL with backups
- [ ] Configure Redis cluster
- [ ] Create ECR repositories for container images
- [ ] Set up AWS Secrets Manager for credentials
- [ ] Document infrastructure architecture

**Day 5: CI/CD Pipeline**
- [ ] Configure GitHub Actions for backend tests
- [ ] Configure GitHub Actions for frontend build
- [ ] Set up Docker multi-stage build
- [ ] Create deployment workflow (staging environment)
- [ ] Configure AWS credentials in GitHub Secrets

**Week 2: Basic API**

**Days 6-8: FastAPI Skeleton**
- [ ] Create FastAPI application structure
- [ ] Set up database connection (SQLAlchemy)
- [ ] Implement health check endpoint (/health, /ready)
- [ ] Add request logging middleware
- [ ] Configure CORS for development
- [ ] Write unit tests for health endpoints

**Days 9-10: Deployment**
- [ ] Build and push Docker image to ECR
- [ ] Deploy to ECS Fargate (staging)
- [ ] Configure ALB and target groups
- [ ] Set up Route53 DNS records
- [ ] Verify deployment (health checks passing)

**Phase 1 Deliverables**:
- ✅ Working infrastructure (VPC, RDS, Redis, ECS)
- ✅ Deployable API (hello-world + health checks)
- ✅ CI/CD pipeline (automated tests + deployment)
- ✅ Documentation (README, architecture diagrams)

**Phase 1 Acceptance Criteria**:
```bash
# Can deploy to staging with one command
git push origin main
# → GitHub Actions runs
# → Tests pass
# → Docker image builds
# → Deploys to ECS
# → Health check returns 200 OK

# Can access API
curl https://api-staging.example.com/health
# → {"status": "healthy", "version": "0.1.0"}

Phase 2: Core Features (Weeks 3-6)

Goal: User authentication and basic CRUD operations

Week 3: Authentication Foundation

Days 11-13: User Model & Database

  • Define User model (SQLAlchemy)
  • Create Alembic migration for users table
  • Implement password hashing (bcrypt)
  • Add user repository layer (CRUD operations)
  • Write unit tests for user repository

Days 14-15: Registration & Login

  • Implement POST /auth/register endpoint
  • Add email validation and uniqueness check
  • Implement POST /auth/login endpoint
  • Generate JWT access + refresh tokens
  • Add token validation middleware
  • Write integration tests for auth flows

Week 4: OAuth2 & Token Management

Days 16-18: OAuth2 Implementation

  • Set up OAuth2 authorization code flow
  • Implement token refresh endpoint
  • Add token blacklist (Redis)
  • Implement logout endpoint
  • Add "Get Current User" endpoint (GET /auth/me)
  • Write security tests (invalid tokens, expired tokens)

Days 19-20: Password Reset

  • Implement password reset request (email token)
  • Set up email service integration (SendGrid/SES)
  • Implement password reset confirmation
  • Add rate limiting for reset requests
  • Write E2E tests for password reset flow

Week 5-6: Resource CRUD

Days 21-25: Core Business Logic

  • Define Resource model (SQLAlchemy)
  • Create Alembic migration for resources table
  • Implement CRUD endpoints (GET, POST, PUT, DELETE /api/v1/resources)
  • Add authorization checks (owner-only access)
  • Implement pagination and filtering
  • Write unit tests for business logic

Days 26-30: Frontend Integration

  • Create React components for authentication
  • Implement login/register forms
  • Add JWT storage and auto-refresh
  • Create resource list and detail views
  • Add create/edit/delete forms
  • Write frontend integration tests

Phase 2 Deliverables:

  • ✅ Complete authentication system (register, login, logout, password reset)
  • ✅ Core CRUD API for resources
  • ✅ Frontend UI for authentication and resource management
  • ✅ Integration tests covering happy paths and error cases

Phase 2 Acceptance Criteria:

# Can register a new user
curl -X POST https://api-staging.example.com/auth/register \
  -d '{"email": "user@example.com", "password": "SecurePass123!"}'
# → {"id": "uuid", "email": "user@example.com", "access_token": "jwt..."}

# Can create a resource
curl -X POST https://api-staging.example.com/api/v1/resources \
  -H "Authorization: Bearer jwt..." \
  -d '{"name": "My Resource", "description": "Test"}'
# → {"id": "uuid", "name": "My Resource", ...}

Phase 3: Hardening (Weeks 7-8)

Goal: Production-ready quality, security, and testing

Week 7: Testing & Coverage

Days 31-33: Test Suite Expansion

  • Achieve 80% unit test coverage (backend)
  • Achieve 80% unit test coverage (frontend)
  • Add integration tests for all API endpoints
  • Implement E2E tests (Playwright/Cypress)
  • Add performance tests (Locust/k6)

Days 34-35: Security Hardening

  • Run OWASP ZAP security scan
  • Fix identified vulnerabilities
  • Add dependency vulnerability scanning (Dependabot/Snyk)
  • Implement rate limiting on all endpoints
  • Add request size limits

Week 8: Code Review & Refinement

Days 36-38: Code Quality

  • Refactor code based on linter feedback
  • Add missing type hints (mypy --strict passes)
  • Optimize database queries (N+1 query checks)
  • Add database indexes for performance
  • Review and update error handling

Days 39-40: Documentation

  • Generate OpenAPI documentation (Swagger UI)
  • Write API integration guide
  • Document environment variables
  • Create deployment runbook
  • Add troubleshooting guide

Phase 3 Deliverables:

  • ✅ Test coverage >= 80% (unit, integration, E2E)
  • ✅ Security scan passing (no high/critical vulnerabilities)
  • ✅ Performance benchmarks documented
  • ✅ Complete API documentation

Phase 3 Acceptance Criteria:

# Tests pass with high coverage
pytest --cov=src --cov-report=term-missing
# → 82% coverage

# Security scan passes
zap-cli quick-scan https://api-staging.example.com
# → No high/critical findings

# Performance benchmarks met
k6 run performance/load-test.js
# → p95 response time < 200ms

Phase 4: Production Deployment (Weeks 9-12)

Goal: Production environment, monitoring, and operational readiness

Week 9-10: Production Infrastructure

Days 41-45: Production Setup

  • Create production VPC and subnets
  • Set up production RDS with multi-AZ
  • Configure Redis cluster with replication
  • Set up CloudFront CDN for static assets
  • Enable WAF rules (OWASP Core Rule Set)
  • Configure auto-scaling policies

Days 46-50: Monitoring & Observability

  • Set up CloudWatch dashboards
  • Configure CloudWatch alarms (error rate, latency, saturation)
  • Integrate Sentry for error tracking
  • Add structured logging (JSON format)
  • Set up log aggregation (CloudWatch Logs Insights)
  • Create on-call runbooks

Week 11-12: Launch Preparation

Days 51-55: Final Testing

  • Perform load testing on production environment
  • Run disaster recovery drill (database restore)
  • Test auto-scaling behavior
  • Verify backup and restore procedures
  • Conduct security penetration testing

Days 56-60: Documentation & Training

  • Finalize deployment documentation
  • Create operational runbooks
  • Write incident response procedures
  • Train team on monitoring and alerts
  • Prepare launch checklist

Phase 4 Deliverables:

  • ✅ Production environment deployed
  • ✅ Monitoring and alerting configured
  • ✅ Operational runbooks complete
  • ✅ Team trained on production operations

Phase 4 Acceptance Criteria:

# Production deployment successful
curl https://api.example.com/health
# → {"status": "healthy", "environment": "production"}

# Monitoring dashboards accessible
open https://console.aws.amazon.com/cloudwatch/dashboards/api-production

# Alerts configured and tested
aws cloudwatch put-metric-data --namespace "API" --metric-name "TestAlert" --value 100
# → PagerDuty alert received

Risk Mitigation

Risk Probability Impact Mitigation Strategy
Database migration fails in production Low High Test migrations on staging data, maintain rollback scripts
Third-party API downtime (email service) Medium Medium Implement retry logic, queue email tasks, have backup provider
Security vulnerability discovered Medium High Automated dependency scanning, regular security audits, bug bounty program
Performance degradation under load Medium High Load testing in phase 3, auto-scaling configured, caching strategy
Team member unavailable Low Medium Documentation, code reviews, knowledge sharing sessions

Dependency Management

External Dependencies:

  • Email service (SendGrid/AWS SES) - Critical path: Week 4
  • Domain registration and SSL certificates - Critical path: Week 1
  • Third-party OAuth providers (Google, GitHub) - Optional: Post-MVP

Internal Dependencies:

  • Infrastructure must be complete before API deployment
  • Authentication must work before resource CRUD
  • Testing framework must be set up before Phase 3
**Rationale**:
- Phased approach reduces risk
- Clear milestones enable tracking
- Day-by-day breakdown prevents ambiguity
- Acceptance criteria make "done" objective
- Risk mitigation shows contingency planning

#### Section 7: Quality Gates (Lines 551-650)

**Purpose**: Define automated checks and standards enforcement

```markdown
## Quality Gates

### Overview

Quality gates are automated checks that must pass before code merges or deploys. They enforce standards and prevent regressions.

### Pre-Commit Gates (Local)

**Triggered**: On `git commit`
**Tool**: pre-commit framework

**Checks**:
1. **Code Formatting**
   - Backend: Black (line length 100)
   - Frontend: Prettier (default config)
   - **Failure Action**: Auto-fix and re-stage

2. **Linting**
   - Backend: Ruff (all rules enabled)
   - Frontend: ESLint (airbnb-typescript config)
   - **Failure Action**: Block commit, display errors

3. **Type Checking**
   - Backend: mypy --strict
   - Frontend: tsc --noEmit
   - **Failure Action**: Block commit, display errors

4. **Security Scanning**
   - Detect hardcoded secrets (detect-secrets)
   - Check for common vulnerabilities (bandit for Python)
   - **Failure Action**: Block commit, display findings

5. **Trailing Whitespace & File Size**
   - Remove trailing whitespace
   - Prevent large files (> 500KB)
   - **Failure Action**: Auto-fix or block

**Configuration**:
```yaml
# .pre-commit-config.yaml
repos:
  - repo: https://github.com/psf/black
    rev: 23.12.0
    hooks:
      - id: black
        args: [--line-length=100]

  - repo: https://github.com/charliermarsh/ruff-pre-commit
    rev: v0.1.8
    hooks:
      - id: ruff

  - repo: https://github.com/pre-commit/mirrors-mypy
    rev: v1.7.1
    hooks:
      - id: mypy
        args: [--strict]

  - repo: https://github.com/Yelp/detect-secrets
    rev: v1.4.0
    hooks:
      - id: detect-secrets

Pull Request Gates (CI)

Triggered: On pull request creation/update Tool: GitHub Actions

Required Checks (all must pass):

  1. Unit Tests
  2. Backend: pytest with coverage >= 80%
  3. Frontend: vitest with coverage >= 80%

  4. Failure Action: Block merge

  5. Integration Tests

  6. API integration tests (all endpoints)
  7. Database migration tests (up/down)

  8. Failure Action: Block merge

  9. Security Scanning

  10. Dependency vulnerabilities (Snyk/Dependabot)
  11. Static analysis (Semgrep/CodeQL)
  12. Container image scanning (Trivy)

  13. Failure Action: Block merge on high/critical

  14. Build Validation

  15. Docker image builds successfully
  16. No build warnings
  17. Image size < 500MB (compressed)

  18. Failure Action: Block merge

  19. Code Quality Metrics

  20. Maintainability index >= 70 (SonarQube)
  21. No code smells or duplications
  22. Complexity <= 10 per function
  23. Failure Action: Warning (not blocking)

GitHub Actions Workflow:

# .github/workflows/pr-checks.yml
name: Pull Request Checks

on:
  pull_request:
    branches: [main, develop]

jobs:
  test-backend:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'
      - name: Install dependencies
        run: |
          pip install poetry
          poetry install
      - name: Run tests
        run: poetry run pytest --cov=src --cov-report=xml --cov-fail-under=80
      - name: Upload coverage
        uses: codecov/codecov-action@v3

  test-frontend:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Set up Node
        uses: actions/setup-node@v4
        with:
          node-version: '20'
      - name: Install dependencies
        run: npm ci
      - name: Run tests
        run: npm run test:coverage -- --coverage.lines=80

  security-scan:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - name: Run Trivy scanner
        uses: aquasecurity/trivy-action@master
        with:
          scan-type: 'fs'
          severity: 'HIGH,CRITICAL'
          exit-code: '1'

Deployment Gates (Staging)

Triggered: On merge to main branch Tool: GitHub Actions + AWS

Required Checks:

  1. All PR Gates (must have passed)

  2. E2E Tests

  3. Full user journeys (register → login → CRUD → logout)
  4. Cross-browser testing (Chrome, Firefox, Safari)

  5. Failure Action: Rollback deployment

  6. Performance Tests

  7. Load test (100 concurrent users)
  8. Response time p95 < 200ms
  9. Error rate < 1%

  10. Failure Action: Rollback deployment

  11. Database Migration

  12. Run Alembic migrations
  13. Verify migrations reversible

  14. Failure Action: Halt deployment

  15. Health Checks

  16. Service /health endpoint returns 200
  17. Database connectivity confirmed
  18. Redis connectivity confirmed
  19. Failure Action: Rollback deployment

Deployment Workflow:

# .github/workflows/deploy-staging.yml
name: Deploy to Staging

on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - name: Build and push Docker image
        # ... (build steps)

      - name: Deploy to ECS
        # ... (deployment steps)

      - name: Run database migrations
        run: |
          aws ecs run-task --task-definition migrate-db
          # Wait for completion

      - name: Wait for service stability
        run: |
          aws ecs wait services-stable --cluster staging --services api

      - name: Run E2E tests
        run: npm run test:e2e -- --env=staging

      - name: Run performance tests
        run: k6 run performance/load-test.js

      - name: Verify health checks
        run: |
          curl -f https://api-staging.example.com/health || exit 1

Production Deployment Gates

Triggered: Manual approval after staging validation Tool: GitHub Actions (manual approval) + AWS CodeDeploy

Required Checks:

  1. Staging Validation
  2. All staging gates passed
  3. Manual QA sign-off
  4. Security review approval

  5. Failure Action: Do not deploy

  6. Blue/Green Deployment

  7. New version deployed to "green" environment
  8. Health checks pass on green
  9. Traffic gradually shifted (10% → 50% → 100%)
  10. Rollback if error rate increases

  11. Failure Action: Automatic rollback

  12. Monitoring Validation

  13. CloudWatch alarms not triggered
  14. Error rate < 0.5%
  15. Latency within SLA (p95 < 300ms)

  16. Failure Action: Halt traffic shift

  17. Database Migration (if applicable)

  18. Backward-compatible migration deployed first
  19. Data integrity checks pass
  20. Rollback plan tested
  21. Failure Action: Manual intervention required

Production Deployment Checklist:

- [ ] All staging gates passed
- [ ] Security review completed
- [ ] Load testing results reviewed
- [ ] Rollback plan documented
- [ ] On-call engineer notified
- [ ] Deployment window scheduled (off-peak hours)
- [ ] Database backup verified (< 1 hour old)
- [ ] Monitoring dashboards open
- [ ] Incident response team on standby

Quality Metrics Dashboard

Tracked Metrics:

Metric Target Current Trend Tool
Unit Test Coverage (Backend) >= 80% 78% ⬆️ pytest-cov
Unit Test Coverage (Frontend) >= 80% 65% ⬆️ Vitest
Integration Test Coverage >= 70% 70% → Custom reports
Security Vulnerabilities (High/Critical) 0 0 → Snyk
Code Maintainability Index >= 70 75 → SonarQube
API Response Time (p95) < 200ms 185ms ⬇️ CloudWatch
Error Rate < 1% 0.3% → Sentry
Deployment Frequency >= 2/week 3/week → GitHub
Mean Time to Recovery (MTTR) < 1 hour 45 min ⬇️ PagerDuty

Failure Recovery Procedures

If Pre-Commit Fails:

  1. Review errors displayed
  2. Fix issues manually or use auto-fix suggestions
  3. Re-stage files (git add)
  4. Retry commit

If PR Checks Fail:

  1. Review GitHub Actions logs
  2. Reproduce locally (act for GitHub Actions simulation)
  3. Fix issues and push
  4. Checks re-run automatically

If Deployment Fails:

  1. Automatic rollback triggered
  2. Incident created in PagerDuty
  3. Review deployment logs
  4. Fix issues and redeploy

If Production Monitoring Alerts:

  1. Evaluate severity (P1-P4)
  2. Check runbook for issue
  3. Roll back if critical
  4. Post-mortem within 48 hours
**Rationale**:
- Automated gates prevent human error
- Clear failure actions reduce ambiguity
- Metrics dashboard provides visibility
- Recovery procedures reduce MTTR
- Production checklist ensures readiness

#### Section 8-12: Additional Sections (Lines 651-900)

Due to length, I'll provide an outline for the remaining sections:

**Section 8: Security Implementation (Lines 651-720)**
- Authentication architecture
- Authorization model (RBAC)
- Secrets management approach
- Data encryption (at rest, in transit)
- Security scanning tools
- Compliance checklist (NIST controls)

**Section 9: Testing Strategy (Lines 721-780)**
- Test pyramid (unit, integration, E2E)
- Coverage targets per layer
- Testing tools and frameworks
- Test data management
- Performance testing approach

**Section 10: Deployment Strategy (Lines 781-840)**
- Environment topology (dev, staging, prod)
- Blue/green deployment process
- Rollback procedures
- Database migration strategy
- Feature flags implementation

**Section 11: Monitoring & Operations (Lines 841-880)**
- Observability stack (logs, metrics, traces)
- Alerting rules and escalation
- Dashboard configuration
- Incident response procedures
- SLA definitions

**Section 12: Team & Responsibilities (Lines 881-900)**
- Team roster with roles
- RACI matrix for key decisions
- Communication channels
- Meeting cadence
- Escalation paths

---

### Key Design Decisions (PROJECT_PLAN_TEMPLATE.md)

#### Decision 1: Comprehensive but Scannable

**Rationale**: Plans serve multiple audiences (developers, managers, compliance).

**Implementation**:
- Quick navigation links at top
- Tables for dense information
- Code blocks for technical details
- Checklists for actionable items

#### Decision 2: Living Document

**Rationale**: Plans evolve as projects progress.

**Implementation**:
- Version number and last updated date
- Status indicators (✅ 🟡 ⚪ ❌)
- Trend indicators (⬆️ → ⬇️)
- Clear sections for updates

#### Decision 3: Evidence-Based

**Rationale**: Claims without evidence create distrust.

**Implementation**:
- Token counts explicitly measured
- Performance targets with monitoring tools
- Security controls with NIST mapping
- Test coverage with tool output

#### Decision 4: Action-Oriented

**Rationale**: Plans exist to drive implementation.

**Implementation**:
- Checklists for every phase
- Acceptance criteria for milestones
- Copy-paste commands for verification
- Clear next steps

---

## Integration Points

### How KICKSTART_REPO.md Generates PROJECT_PLAN.md

**Flow**:

User fills KICKSTART_REPO.md ↓ User pastes to LLM (Claude/ChatGPT/Gemini) ↓ LLM analyzes repo + user input ↓ LLM detects tech stack ↓ LLM recommends standards (@load directives) ↓ LLM generates PROJECT_PLAN.md using template structure ↓ User saves PROJECT_PLAN.md to repo ↓ PROJECT_PLAN.md becomes source of truth

**Example LLM Prompt** (embedded in KICKSTART_REPO.md):
```markdown
Generate PROJECT_PLAN.md with these sections:
1. Project Overview (from my project description)
2. Tech Stack Analysis (from detected files)
3. Standards Alignment (using @load syntax)
4. System Architecture (based on detected patterns)
5. Implementation Phases (12-week timeline)
6. Quality Gates (automated checks)
7. Security Implementation (NIST controls)
8. Testing Strategy (80% coverage)
9. Deployment Strategy (blue/green)
10. Monitoring (CloudWatch, Sentry)
11. Team Roles (from stakeholders)

How Templates Integrate with Standards Repository

Direct Integration (if standards repo available):

# User runs skill-loader
python3 /path/to/standards/scripts/skill-loader.py recommend ./my-project

# Output:
# Recommended skills:
# - coding-standards (Level 1: 336 tokens)
# - security-practices (Level 1: 409 tokens)
# - testing (Level 1: 430 tokens)
#
# Load command:
# @load [product:api + CS:python + TS:pytest + SEC:*]

Indirect Integration (templates work standalone):

  • Templates embed standards URLs
  • LLMs fetch standards via web search (if capable)
  • Users manually copy relevant sections
  • Fallback to embedded best practices

Usage Flow Examples

Example 1: New Project from Scratch

Scenario: User wants to build a Python API, has no code yet

Steps:

  1. Create empty Git repository
  2. Copy KICKSTART_REPO.md to repo root
  3. Fill in:
Project Name: Task Manager API
Project Type: API
Primary Language: Python
Tech Stack:
  Backend: FastAPI
  Database: PostgreSQL
  1. Paste entire file to Claude
  2. Claude generates:
  3. Tech Stack Analysis (Python, FastAPI, PostgreSQL)
  4. Standards: @load [product:api + CS:python + TS:pytest + SEC:*]
  5. PROJECT_PLAN.md with 12-week timeline
  6. Sample configs (pyproject.toml, Dockerfile, .github/workflows/ci.yml)
  7. User saves PROJECT_PLAN.md
  8. User creates files from generated configs
  9. User runs git commit (pre-commit gates auto-configure)

Time: 10 minutes from empty repo to first commit

Example 2: Existing Project Needs Documentation

Scenario: User has 3-month-old React app, needs to document it

Steps:

  1. Copy KICKSTART_REPO.md to existing repo
  2. Leave all fields blank (triggers auto-detection)
  3. Paste entire file + instruction "This repo has existing code, please analyze it" to ChatGPT
  4. ChatGPT scans:
  5. package.json → React, TypeScript, Vite
  6. src/components/ → Component structure
  7. .github/workflows/ → Existing CI/CD
  8. ChatGPT generates:
  9. Tech Stack Analysis (detected state)
  10. Standards: @load [product:frontend-web + FE:react + TS:vitest]
  11. PROJECT_PLAN.md documenting current state
  12. Gap analysis (missing tests, no E2E, no performance monitoring)
  13. Recommended improvements
  14. User saves PROJECT_PLAN.md as documentation
  15. User follows gap analysis to improve project

Time: 5 minutes to get comprehensive analysis

Example 3: Compliance-Heavy Project

Scenario: User building HIPAA-compliant healthcare API

Steps:

  1. Copy KICKSTART_REPO.md to repo
  2. Fill in:
Project Name: Patient Portal API
Project Type: API
Primary Language: Python
Compliance Needs: HIPAA
  1. Paste to Claude (with HIPAA context)
  2. Claude generates:
  3. Standards: @load [product:api + CS:python + SEC:* + NIST-IG:base + LEG:healthcare]
  4. PROJECT_PLAN.md with HIPAA section
  5. NIST 800-53r5 control mapping
  6. Encryption requirements (at rest, in transit)
  7. Audit logging specifications
  8. BAA (Business Associate Agreement) checklist
  9. User saves PROJECT_PLAN.md
  10. User implements according to compliance requirements

Time: 15 minutes to get compliance roadmap

Success Criteria

Template Effectiveness Metrics

KICKSTART_REPO.md Success:

  • ✅ User can start using in < 30 seconds
  • ✅ Works without filling any fields (auto-detection)
  • ✅ Works with any LLM (Claude, ChatGPT, Gemini)
  • ✅ Generates complete PROJECT_PLAN.md
  • ✅ Produces working starter files

PROJECT_PLAN_TEMPLATE.md Success:

  • ✅ Comprehensive (covers all aspects of project)
  • ✅ Scannable (quick navigation, tables)
  • ✅ Actionable (checklists, commands)
  • ✅ Maintainable (version tracking, status indicators)
  • ✅ Standards-aligned (explicit @load directives)

User Experience Validation

Test 1: New User (No Context)

  • Give templates to developer who's never seen standards repo
  • Measure: Time to generate PROJECT_PLAN.md
  • Target: < 10 minutes

Test 2: LLM Compatibility

  • Test KICKSTART_REPO.md with Claude, ChatGPT, Gemini
  • Measure: Quality of generated PROJECT_PLAN.md
  • Target: All LLMs produce usable output (80%+ complete)

Test 3: Real Project

  • Have team use templates for actual project
  • Measure: Adoption rate, satisfaction score
  • Target: ⅘ satisfaction, continued use after trial

Next Steps

Immediate Actions (Design Complete)

  1. Review this architecture document
  2. Validate structure aligns with requirements
  3. Confirm examples are realistic

  4. Verify integration points

  5. Get feedback on design

  6. Share with stakeholders
  7. Identify gaps or concerns

  8. Refine based on input

  9. Proceed to implementation

  10. Create templates/KICKSTART_REPO.md
  11. Create templates/PROJECT_PLAN_TEMPLATE.md
  12. Test with real scenarios

Implementation Checklist

  • Architecture review complete
  • Design approved by stakeholders
  • Implementation plan created
  • Templates written
  • Templates tested with multiple LLMs
  • Documentation updated (README.md)
  • Examples created (at least 3 product types)
  • User guide written

Appendix: Design Rationale Summary

Why These Structures?

KICKSTART_REPO.md Design:

  • Standalone: Works without external dependencies
  • Progressive: Quick Start → Basics → Advanced → Fallback
  • LLM-Optimized: Clear instructions, explicit examples, structured output
  • Fill-in-the-Blank: Reduces friction, provides guidance
  • Auto-Detection: Works even with blank fields

PROJECT_PLAN_TEMPLATE.md Design:

  • Comprehensive: Covers all aspects developers need
  • Scannable: Tables, quick nav, clear sections
  • Living Document: Versioned, status-tracked, maintainable
  • Evidence-Based: Measurable metrics, explicit tools
  • Action-Oriented: Checklists, commands, acceptance criteria

What Makes This Production-Ready?

  1. Real-World Tested Concepts: Based on existing KICKSTART_PROMPT.md and project_plan_example.md
  2. Multi-LLM Compatible: No tool-specific syntax (except optional sections)
  3. Progressive Disclosure: Works for quick starts and comprehensive planning
  4. Standards-Integrated: Explicit @load directives, token efficiency
  5. Self-Documenting: Examples, rationales, validation built-in

Document Status: Ready for Implementation Next Action: Proceed to template creation