backend-development

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems. | Sử dụng khi xây dựng API, server, backend, máy chủ, xử lý dữ liệu, endpoint, microservices.

16 stars

Best use case

backend-development is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems. | Sử dụng khi xây dựng API, server, backend, máy chủ, xử lý dữ liệu, endpoint, microservices.

Teams using backend-development should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/backend-development/SKILL.md --create-dirs "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/backend/backend-development/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/backend-development/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How backend-development Compares

Feature / Agentbackend-developmentStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems. | Sử dụng khi xây dựng API, server, backend, máy chủ, xử lý dữ liệu, endpoint, microservices.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Backend Development Skill

Production-ready backend development with modern technologies, best practices, and proven patterns.

## When to Use

- Designing RESTful, GraphQL, or gRPC APIs
- Building authentication/authorization systems
- Optimizing database queries and schemas
- Implementing caching and performance optimization
- OWASP Top 10 security mitigation
- Designing scalable microservices
- Testing strategies (unit, integration, E2E)
- CI/CD pipelines and deployment
- Monitoring and debugging production systems

## Technology Selection Guide

**Languages:** Node.js/TypeScript (full-stack), Python (data/ML), Go (concurrency), Rust (performance)
**Frameworks:** NestJS, FastAPI, Django, Express, Gin
**Databases:** PostgreSQL (ACID), MongoDB (flexible schema), Redis (caching)
**APIs:** REST (simple), GraphQL (flexible), gRPC (performance)

See: `references/backend-technologies.md` for detailed comparisons

## Reference Navigation

**Core Technologies:**
- `backend-technologies.md` - Languages, frameworks, databases, message queues, ORMs
- `backend-api-design.md` - REST, GraphQL, gRPC patterns and best practices

**Security & Authentication:**
- `backend-security.md` - OWASP Top 10 2025, security best practices, input validation
- `backend-authentication.md` - OAuth 2.1, JWT, RBAC, MFA, session management

**Performance & Architecture:**
- `backend-performance.md` - Caching, query optimization, load balancing, scaling
- `backend-architecture.md` - Microservices, event-driven, CQRS, saga patterns

**Quality & Operations:**
- `backend-testing.md` - Testing strategies, frameworks, tools, CI/CD testing
- `backend-code-quality.md` - SOLID principles, design patterns, clean code
- `backend-devops.md` - Docker, Kubernetes, deployment strategies, monitoring
- `backend-debugging.md` - Debugging strategies, profiling, logging, production debugging
- `backend-mindset.md` - Problem-solving, architectural thinking, collaboration

## Key Best Practices (2025)

**Security:** Argon2id passwords, parameterized queries (98% SQL injection reduction), OAuth 2.1 + PKCE, rate limiting, security headers

**Performance:** Redis caching (90% DB load reduction), database indexing (30% I/O reduction), CDN (50%+ latency cut), connection pooling

**Testing:** 70-20-10 pyramid (unit-integration-E2E), Vitest 50% faster than Jest, contract testing for microservices, 83% migrations fail without tests

**DevOps:** Blue-green/canary deployments, feature flags (90% fewer failures), Kubernetes 84% adoption, Prometheus/Grafana monitoring, OpenTelemetry tracing

## Quick Decision Matrix

| Need | Choose |
|------|--------|
| Fast development | Node.js + NestJS |
| Data/ML integration | Python + FastAPI |
| High concurrency | Go + Gin |
| Max performance | Rust + Axum |
| ACID transactions | PostgreSQL |
| Flexible schema | MongoDB |
| Caching | Redis |
| Internal services | gRPC |
| Public APIs | GraphQL/REST |
| Real-time events | Kafka |

## Implementation Checklist

**API:** Choose style → Design schema → Validate input → Add auth → Rate limiting → Documentation → Error handling

**Database:** Choose DB → Design schema → Create indexes → Connection pooling → Migration strategy → Backup/restore → Test performance

**Security:** OWASP Top 10 → Parameterized queries → OAuth 2.1 + JWT → Security headers → Rate limiting → Input validation → Argon2id passwords

**Testing:** Unit 70% → Integration 20% → E2E 10% → Load tests → Migration tests → Contract tests (microservices)

**Deployment:** Docker → CI/CD → Blue-green/canary → Feature flags → Monitoring → Logging → Health checks

## Resources

- OWASP Top 10: https://owasp.org/www-project-top-ten/
- OAuth 2.1: https://oauth.net/2.1/
- OpenTelemetry: https://opentelemetry.io/

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