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senior-architect

@ak-eyther/model-project
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Software architecture guidance for designing scalable systems. Covers architecture patterns, tech stack decisions, system design trade-offs, and integration patterns.

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SKILL.md

name senior-architect
description Software architecture guidance for designing scalable systems. Covers architecture patterns, tech stack decisions, system design trade-offs, and integration patterns.

Note: Current UI is Next.js on Vercel: https://{{FRONTEND_URL}} (production app) and https://{{STAGING_FRONTEND_URL}} (staging).

Senior Architect

Guidance for architecture decisions in {{PROJECT_NAME}} and similar projects.

When to Use

  • System architecture decisions (monolith vs microservices, sync vs async)
  • Tech stack selection and trade-offs
  • Database design and data modeling
  • API design patterns (REST, GraphQL, event-driven)
  • Scalability and performance architecture
  • Integration patterns between services

Architecture Decision Framework

1. Understand Requirements

Functional Requirements    → What the system must do
Non-Functional Requirements → How well it must do it
├── Performance (latency, throughput)
├── Scalability (users, data volume)
├── Availability (uptime, redundancy)
├── Security (auth, encryption)
└── Maintainability (team size, deployment frequency)

2. Evaluate Trade-offs

Decision Option A Option B Choose A When Choose B When
Sync vs Async REST API Message Queue Low latency needed Decoupling, reliability
Monolith vs Microservices Single deploy Many services Small team, early stage Scale teams independently
SQL vs NoSQL PostgreSQL MongoDB/Redis Relations, ACID Flexible schema, speed
Server vs Serverless Railway/EC2 Lambda/Functions Predictable load, state Spiky load, stateless

3. Document Decisions (ADR Format)

# ADR-001: Use PostgreSQL for Campaign Data

## Status: Accepted

## Context
Campaign data has complex relationships (offers → lists → segments).
Need ACID for financial calculations (EPC, revenue).

## Decision
Use PostgreSQL with proper indexing for analytics queries.

## Consequences
+ Strong consistency for revenue calculations
+ Complex joins for analytics
- Need to manage connection pooling
- Schema migrations require planning

Common Patterns

API Layer (FastAPI)

Request → Middleware → Router → Service → Repository → Database
                ↓
           Validation (Pydantic)
                ↓
           Response

Data Pipeline (Batch)

Source (Google Sheets) → Extract → Transform → Load → PostgreSQL
                              ↓
                         Validate
                              ↓
                         Log metrics

AI Agent Architecture

User Query → Orchestrator Agent
                 ↓
         Tool Selection
         ├── SQL Tool (structured data)
         ├── Memory Tool (patterns, history)
         └── Search Tool (web, docs)
                 ↓
         Analyst Agent (reasoning)
                 ↓
         Judge Agent (validation)
                 ↓
         Response

{{PROJECT_NAME}} Tech Stack

Layer Technology Why
Frontend Next.js (Vercel) Production app UI with fast iteration
Backend FastAPI Async, Pydantic, OpenAPI docs
Database PostgreSQL ACID, complex analytics queries
Memory ChromaDB Semantic search for patterns
LLM Claude (Anthropic) Best reasoning, tool use
Deploy Railway Simple, auto-deploy from git

Anti-Patterns to Avoid

  1. Premature optimization - Profile first, optimize second
  2. Over-engineering - Start simple, add complexity when needed
  3. Distributed transactions - Use saga pattern or avoid
  4. Shared mutable state - Use message passing instead
  5. God classes - Split by responsibility

Quick Reference

# Check system dependencies
python -c "import sys; print(sys.version)"
pip list | grep -E "fastapi|sqlalchemy|chromadb"

# Database schema
alembic heads  # Check migration state
alembic upgrade head  # Apply migrations

# Service health
curl http://localhost:8000/health