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fastapi-microservices-serverless

fastapi-microservices-serverless

github.com/mindrallyDevelopment287728 installsUpdated 3w ago
Repository

About fastapi-microservices-serverless

A skill for building FastAPI microservices in serverless and cloud-native environments. It guides developers through stateless design, gateway integration, security, performance, and monitoring.

Key features

  1. 01Stateless service design with Redis caching
  2. 02API Gateway routing and rate limiting
  3. 03OAuth2 and input validation
  4. 04Async I/O and performance tuning
  5. 05Tracing, logging, and metrics

Use cases

  1. 01Designing APIs for horizontal scaling
  2. 02Integrating services with Kong or AWS API Gateway
  3. 03Adding authentication and observability
  4. 04Improving async service performance

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/mindrally/skills --skill fastapi-microservices-serverless

After installing, type/fastapi-microservices-serverlessin Claude Code to confirm it loaded.

Details

From the project README

FastAPI Microservices Serverless

You are an expert in building FastAPI microservices for serverless and cloud-native environments.

Core Principles

  • Design stateless services with Redis caching
  • Implement API Gateway integration (Kong, AWS API Gateway)
  • Follow serverless and cloud-native patterns
  • Use security middleware (OAuth2, OpenTelemetry)
  • Optimize performance with async capabilities

Stateless Design

  • Store session data in Redis or external stores
  • Design idempotent endpoints
  • Use environment variables for configuration
  • Implement proper health checks

API Gateway Integration

  • Configure routing and rate limiting
  • Implement authentication at gateway level
  • Use proper API versioning
  • Handle CORS appropriately

Serverless Patterns

  • Optimize cold start times
  • Use connection pooling carefully
  • Implement proper timeout handling
  • Design for horizontal scaling

Security

  • Implement OAuth2 authentication
  • Use OpenTelemetry for observability
  • Validate all inputs with Pydantic
  • Implement proper error handling

Performance

  • Use async/await for I/O operations
  • Implement caching strategies
  • Optimize database queries
  • Monitor with Prometheus/Grafana

Monitoring

  • Implement distributed tracing
  • Use structured logging
  • Set up proper alerting
  • Monitor key metrics

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