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architecting-omega-systems

@doanchienthangdev/omgkit
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Designs scalable, resilient architectures using the 7 Omega principles. Use when building systems that need to scale to millions of users or designing platform-level infrastructure.

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SKILL.md

name architecting-omega-systems
description Designs scalable, resilient architectures using the 7 Omega principles. Use when building systems that need to scale to millions of users or designing platform-level infrastructure.
category omega
triggers omega architecture, system design, scalable architecture, distributed systems

Architecting Omega Systems

Build scalable, resilient, and future-proof systems following the 7 Omega Principles for platform-grade architecture.

Quick Start

# 1. Define architectural requirements
Architecture:
  Name: "E-commerce Platform"
  Scale: "1M+ concurrent users"
  Principles: [leverage, abstraction, agentic, antifragile]

# 2. Apply Omega decision framework
Decision:
  Question: "Monolith or microservices?"
  Omega_Check:
    - Leverage: "Does this multiply capability?"
    - Scale: "Zero-marginal-cost at 1000x?"
    - Resilience: "Self-healing under stress?"

# 3. Design with antifragility
Pattern: Event-Driven + Circuit Breakers + Auto-Scaling

Features

Feature Description Guide
7 Omega Principles Core architectural tenets for scalable systems Apply all 7 to every decision
Leverage Architecture Systems that amplify effort, not add to it Build frameworks, not features
Agentic Services Autonomous, self-healing microservices Circuit breakers + health monitors
Zero-Marginal-Cost Platform economics at scale Edge compute + aggressive caching
Event-Driven Design Loose coupling via event bus Kafka/Redis streams + sagas
Antifragile Patterns Systems that strengthen under stress Chaos engineering + adaptive config
Decision Framework Systematic architecture evaluation Omega checklist for every choice

Common Patterns

The 7 Omega Principles

1. LEVERAGE MULTIPLICATION   - Build systems that amplify effort
2. TRANSCENDENT ABSTRACTION  - Solve classes of problems
3. AGENTIC DECOMPOSITION     - Autonomous, self-managing services
4. RECURSIVE IMPROVEMENT     - Systems that optimize themselves
5. ZERO-MARGINAL-COST        - No cost per user at scale
6. ANTIFRAGILE DESIGN        - Grow stronger under stress
7. COMPOSABLE PRIMITIVES     - Lego blocks that combine infinitely

Leverage Architecture Pattern

// ONE implementation handles ALL entity types
class LeveragedEntitySystem {
  async create<T>(type: string, data: T): Promise<T> {
    const validated = await this.validators.get(type)?.validate(data);
    await this.runHooks(type, 'beforeCreate', validated);
    const result = await this.storage.create(type, validated);
    await this.runHooks(type, 'afterCreate', result);
    return result as T;
  }

  // New entity = configuration, not code
  registerEntityType(type: string, config: EntityConfig): void {
    this.validators.set(type, createValidator(config.schema));
  }
}

Agentic Service Pattern

abstract class AgenticService {
  private circuitBreaker: CircuitBreaker;
  private healthMonitor: HealthMonitor;
  private autoScaler: AutoScaler;

  constructor() {
    // Self-monitoring
    this.healthMonitor = new HealthMonitor({
      onUnhealthy: () => this.selfHeal()
    });

    // Self-protecting
    this.circuitBreaker = new CircuitBreaker({
      failureThreshold: 5,
      onOpen: () => this.notifyDegradation()
    });
  }

  protected async executeWithResilience<T>(
    operation: () => Promise<T>,
    fallback?: () => T
  ): Promise<T> {
    return this.circuitBreaker.execute(operation, fallback);
  }
}

Zero-Marginal-Cost Pattern

// Edge-first architecture
const platformConfig = {
  compute: { provider: 'cloudflare-workers', pricing: 'per-invocation' },
  storage: { provider: 'r2', cdn: { enabled: true, ttl: '1y' } },
  database: { provider: 'planetscale', sharding: 'automatic' },
  cache: {
    layers: [
      { type: 'browser', ttl: '1h' },
      { type: 'cdn-edge', ttl: '24h' },
      { type: 'origin', ttl: '5m' }
    ]
  }
};

Event-Driven Saga Pattern

class OrderSaga {
  constructor(eventBus: EventBus) {
    eventBus.subscribe('order.created', this.handleOrderCreated);
    eventBus.subscribe('payment.failed', this.handlePaymentFailed);
  }

  handleOrderCreated = async (event: DomainEvent<Order>) => {
    await this.eventBus.publish({
      type: 'inventory.reserve',
      data: { orderId: event.data.id, items: event.data.items },
      correlationId: event.correlationId
    });
  };

  handlePaymentFailed = async (event: DomainEvent) => {
    // Compensating action
    await this.eventBus.publish({ type: 'inventory.release', ... });
    await this.eventBus.publish({ type: 'order.cancelled', ... });
  };
}

Architecture Decision Template

## Omega Architecture Decision

### Context
[Problem and constraints]

### Omega Principles Check
| Principle | Question | Assessment |
|-----------|----------|------------|
| Leverage | Does this multiply capability? | |
| Abstraction | Solving class of problems? | |
| Agentic | Can it operate autonomously? | |
| Recursive | Will it self-improve? | |
| Zero-Marginal | Sub-linear cost scaling? | |
| Antifragile | Stronger under stress? | |
| Composable | Combines with other components? | |

### Scale Analysis
- 10x load: [Impact]
- 100x load: [Impact]
- 1000x load: [Impact]

### Decision
[Chosen approach with rationale]

Best Practices

Do Avoid
Apply all 7 Omega principles to every decision Building monoliths that cannot decompose
Design for 1000x scale from day one Tight coupling between services
Use event-driven patterns for loose coupling Ignoring failure modes in design
Implement circuit breakers at all boundaries Scaling vertically when horizontal is possible
Build self-healing capabilities into services Hardcoding configuration values
Measure and optimize cost per transaction Skipping capacity planning
Document decisions using ADR template Deploying without health checks
Test failure scenarios with chaos engineering Synchronous calls for non-critical paths
Use infrastructure as code Ignoring data consistency requirements
Implement observability everywhere Underestimating distributed system complexity