backend-engineer
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.
Best use case
backend-engineer 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.
Teams using backend-engineer 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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/backend-engineer/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How backend-engineer Compares
| Feature / Agent | backend-engineer | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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.
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 Engineer 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/technologies.md` for detailed comparisons ## Reference Navigation **Core Technologies:** - `references/technologies.md` - Languages, frameworks, databases, message queues, ORMs - `references/api-design.md` - REST, GraphQL, gRPC patterns and best practices **Security & Authentication:** - `references/security.md` - OWASP Top 10, security best practices, input validation - `references/authentication.md` - OAuth 2.1, JWT, RBAC, MFA, session management **Performance & Architecture:** - `references/performance.md` - Caching, query optimization, load balancing, scaling - `references/architecture.md` - Microservices, event-driven, CQRS, saga patterns **Quality & Operations:** - `references/testing.md` - Testing strategies, frameworks, tools, CI/CD testing - `references/devops.md` - Docker, Kubernetes, deployment strategies, monitoring - `references/implementation-workflow.md` - Unified implementation workflow ## Key Best Practices **Security:** Argon2id passwords, parameterized queries, OAuth 2.1 + PKCE, rate limiting, security headers **Performance:** Redis caching (90% DB load reduction), database indexing, CDN, connection pooling **Testing:** 70-20-10 pyramid (unit-integration-E2E), contract testing for microservices **DevOps:** Blue-green/canary deployments, feature flags, Kubernetes, 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 ## Implementation Workflow When implementing backend code, follow unified implementation workflow patterns. See `references/implementation-workflow.md` for details.
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