Backend Architect Skill - Architecture Intelligence
Searchable database of backend architectures, databases, security guidelines, language best practices, API patterns, and platform engineering recommendations.
Prerequisites
- •Python 3.8+
- •No external dependencies (uses only standard library)
How to Use This Skill
Step 1: Analyze User Requirements
Extract key information from user request:
- •Product type: SaaS, E-commerce, Fintech, Social, Chat, IoT, AI Agent, etc.
- •Scale requirements: Startup (<20 devs), Enterprise (>50 devs), Global scale
- •Critical features: Real-time, High-write, ACID transactions, AI/ML integration
- •Preferred stack: Go, Python, Node, Java, .NET, Rust
Step 2: Search Relevant Domains
Use search.py multiple times to gather comprehensive information:
bash
python3 .claude/skills/backend-architect/scripts/search.py "<keyword>" --domain <domain> [-n <max_results>]
Recommended search order:
- •Product - Get architecture recommendations for product type
- •Architecture - Get detailed architecture pattern (components, trade-offs)
- •Database - Get database recommendations for data model
- •Security - Get security guidelines (OWASP, API Security)
- •Language - Get language/framework recommendations
- •API - Get API pattern recommendations (REST, GraphQL, gRPC)
- •Naming - Get naming conventions for the stack
- •Error - Get error handling patterns
- •Platform - Get DevOps/Platform engineering tools
- •Stack - Get stack-specific guidelines
Step 3: Stack Guidelines
Get stack-specific recommendations:
bash
python3 .claude/skills/backend-architect/scripts/search.py "<keyword>" --stack <stack>
Available stacks: go, python, node, java, dotnet, rust
Step 4: Verify Latest Versions (STRICT)
Before starting work, always verify the latest stable versions of technologies via web search. AI training data has a knowledge cutoff (e.g., Go 1.24 is now out, but AI might only know 1.23).
- •Search Query:
latest stable version <technology> Jan 2026 - •What to check: Language runtimes (Go, Python, Node), Base Docker images, Primary frameworks.
- •Where to apply: Dockerfile, go.mod, package.json, requirements.txt.
Example: If AI suggests Go 1.23 but 1.24 is out, use 1.24-alpine in Dockerfile.
Search Reference
Available Domains
| Domain | Use For | Example Keywords |
|---|---|---|
architecture | System architecture patterns | microservices, modular monolith, serverless, CQRS |
database | Database selection | postgresql, mongodb, redis, vector, timescale |
security | Security best practices | OWASP, injection, auth, encryption, secrets |
product | Product-specific recommendations | ecommerce, saas, fintech, chat, iot, ai agent |
language | Language/framework selection | rust, go, python, node, java, dotnet |
api | API design patterns | rest, graphql, grpc, websocket, mcp |
naming | Naming conventions | python, go, sql, graphql, redis |
error | Error handling patterns | http, grpc, postgres, mongodb, oauth |
platform | DevOps/Platform tools | kubernetes, terraform, argocd, vault |
Available Stacks
| Stack | Focus |
|---|---|
go | Gin/Echo, GORM, Testcontainers, Cloud-native |
python | FastAPI/Django, SQLAlchemy, Pytest, AI/ML |
node | NestJS/Fastify, Prisma, TypeScript, Full-stack |
java | Spring Boot/Quarkus, Virtual Threads, Enterprise |
dotnet | ASP.NET Core, EF Core, Minimal APIs, Cloud-native |
rust | Axum/Actix, SQLx, High-performance, Systems |
Example Workflow
User request: "Build a real-time chat application"
bash
# 1. Get product recommendations python3 .claude/skills/backend-architect/scripts/search.py "chat messaging" --domain product # 2. Get architecture pattern python3 .claude/skills/backend-architect/scripts/search.py "real-time event driven" --domain architecture # 3. Get database recommendations python3 .claude/skills/backend-architect/scripts/search.py "high write throughput cassandra" --domain database # 4. Get language recommendations python3 .claude/skills/backend-architect/scripts/search.py "elixir actor model" --domain language # 5. Get API pattern python3 .claude/skills/backend-architect/scripts/search.py "websocket real-time" --domain api # 6. Get security guidelines python3 .claude/skills/backend-architect/scripts/search.py "api security rate limit" --domain security
Tips for Better Results
- •Search multiple domains for comprehensive recommendations
- •Cross-reference product recommendations with architecture patterns
- •Always check security guidelines before finalizing design
- •Use stack-specific search for implementation details
Common Rules for Professional Backend
Database Selection
- •OLTP (transactions): PostgreSQL, MySQL, CockroachDB
- •High-write (logs/events): Cassandra, ScyllaDB, ClickHouse
- •Caching: Redis, Memcached, Dragonfly
- •Vector (AI/RAG): pgvector, Qdrant, Pinecone
- •Time-series: TimescaleDB, InfluxDB
Architecture Decision Matrix
- •Team < 20: Modular Monolith
- •Team > 50: Microservices
- •Unpredictable traffic: Serverless
- •Real-time: Event-Driven
- •AI/LLM: Agentic Architecture
API Pattern Selection
- •Public API: REST (universal compatibility)
- •Complex frontend: GraphQL (flexible queries)
- •Internal services: gRPC (performance)
- •Real-time bidirectional: WebSocket
- •AI integration: MCP (Model Context Protocol)
Pre-Implementation Checklist
Architecture Quality
- • Is the architecture appropriate for team size?
- • Are trade-offs clearly understood?
- • Is data consistency model defined?
Security
- • OWASP Top 10 addressed?
- • API security (rate limiting, auth)?
- • Secrets management configured?
Scalability
- • Database bottlenecks identified?
- • Horizontal scaling strategy defined?
- • Caching layer planned?
Observability
- • Structured logging configured?
- • Distributed tracing enabled?
- • Metrics and alerting set up?