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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.

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mrgoonie/claudekit-skills
연동 도구DockerKubernetesPostgreSQLMongoDBRedis
설치 방법 보기

포함된 파일(12)

  • SKILL.md4.4 KB
  • references/backend-api-design.md10.4 KB
  • references/backend-architecture.md12.2 KB
  • references/backend-authentication.md9.2 KB
  • references/backend-code-quality.md12.8 KB
  • references/backend-debugging.md18.5 KB
  • references/backend-devops.md10.5 KB
  • references/backend-mindset.md11.0 KB
  • references/backend-performance.md9.4 KB
  • references/backend-security.md7.9 KB
  • references/backend-technologies.md7.5 KB
  • references/backend-testing.md10.4 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

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

NeedChoose
Fast developmentNode.js + NestJS
Data/ML integrationPython + FastAPI
High concurrencyGo + Gin
Max performanceRust + Axum
ACID transactionsPostgreSQL
Flexible schemaMongoDB
CachingRedis
Internal servicesgRPC
Public APIsGraphQL/REST
Real-time eventsKafka

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

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aesthetic

무료

Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

ai-multimodal

무료

Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

better-auth

무료

Implement authentication and authorization with Better Auth - a framework-agnostic TypeScript authentication framework. Features include email/password authentication with verification, OAuth providers (Google, GitHub, Discord, etc.), two-factor authentication (TOTP, SMS), passkeys/WebAuthn support, session management, role-based access control (RBAC), rate limiting, and database adapters. Use when adding authentication to applications, implementing OAuth flows, setting up 2FA/MFA, managing user sessions, configuring authorization rules, or building secure authentication systems for web applications.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

bunny

무료

Integrate Bunny.net services (CDN, Storage, Stream, DNS, Edge Scripting, Shield, Magic Containers, Optimizer, Database). Use when building with Bunny.net APIs, deploying to Bunny CDN, uploading files to Edge Storage, managing video streaming, configuring DNS zones, writing edge scripts, setting up WAF/DDoS protection, deploying containers, or optimizing images. Triggers on "bunny", "bunnycdn", "b-cdn", "pull zone", "edge storage", "bunny stream".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

chrome-devtools

무료

Browser automation, debugging, and performance analysis using Puppeteer CLI scripts. Use for automating browsers, taking screenshots, analyzing performance, monitoring network traffic, web scraping, form automation, and JavaScript debugging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

code-review

무료

Use when receiving code review feedback (especially if unclear or technically questionable), when completing tasks or major features requiring review before proceeding, or before making any completion/success claims. Covers three practices - receiving feedback with technical rigor over performative agreement, requesting reviews via code-reviewer subagent, and verification gates requiring evidence before any status claims. Essential for subagent-driven development, pull requests, and preventing false completion claims.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

mrgoonie/claudekit-skills2.2천2026년 4월 3일 업데이트

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