Back to Skills

processing-api-batches

jeremylongshore
Updated Today
34 views
712
74
712
View on GitHub
Developmentapi

About

This Claude skill efficiently processes bulk API operations by implementing batching, throttling, and parallel execution. It's designed for developers handling large volumes of API calls, triggered with phrases like "process bulk requests" or "batch API calls." The skill provides structured guidance for API design and integration while managing execution constraints.

Documentation

Prerequisites

Before using this skill, ensure you have:

  • API design specifications or requirements documented
  • Development environment with necessary frameworks installed
  • Database or backend services accessible for integration
  • Authentication and authorization strategies defined
  • Testing tools and environments configured

Instructions

Step 1: Design API Structure

Plan the API architecture and endpoints:

  1. Use Read tool to examine existing API specifications from {baseDir}/api-specs/
  2. Define resource models, endpoints, and HTTP methods
  3. Document request/response schemas and data types
  4. Identify authentication and authorization requirements
  5. Plan error handling and validation strategies

Step 2: Implement API Components

Build the API implementation:

  1. Generate boilerplate code using Bash(api:batch-*) with framework scaffolding
  2. Implement endpoint handlers with business logic
  3. Add input validation and schema enforcement
  4. Integrate authentication and authorization middleware
  5. Configure database connections and ORM models

Step 3: Add API Features

Enhance with production-ready capabilities:

  • Implement rate limiting and throttling policies
  • Add request/response logging with correlation IDs
  • Configure error handling with standardized responses
  • Set up health check and monitoring endpoints
  • Enable CORS and security headers

Step 4: Test and Document

Validate API functionality:

  1. Write integration tests covering all endpoints
  2. Generate OpenAPI/Swagger documentation automatically
  3. Create usage examples and authentication guides
  4. Test with various HTTP clients (curl, Postman, REST Client)
  5. Perform load testing to validate performance targets

Output

The skill generates production-ready API artifacts:

API Implementation

Generated code structure:

  • {baseDir}/src/routes/ - Endpoint route definitions
  • {baseDir}/src/controllers/ - Business logic handlers
  • {baseDir}/src/models/ - Data models and schemas
  • {baseDir}/src/middleware/ - Authentication, validation, logging
  • {baseDir}/src/config/ - Configuration and environment variables

API Documentation

Comprehensive API docs including:

  • OpenAPI 3.0 specification with complete endpoint definitions
  • Authentication and authorization flow diagrams
  • Request/response examples for all endpoints
  • Error code reference with troubleshooting guidance
  • SDK generation instructions for multiple languages

Testing Artifacts

Complete test suite:

  • Unit tests for individual controller functions
  • Integration tests for end-to-end API workflows
  • Load test scripts for performance validation
  • Mock data generators for realistic testing
  • Postman/Insomnia collection for manual testing

Configuration Files

Production-ready configs:

  • Environment variable templates (.env.example)
  • Database migration scripts
  • Docker Compose for local development
  • CI/CD pipeline configuration
  • Monitoring and alerting setup

Error Handling

Common issues and solutions:

Schema Validation Failures

  • Error: Request body does not match expected schema
  • Solution: Add detailed validation error messages; provide schema documentation; implement request sanitization

Authentication Errors

  • Error: Invalid or expired authentication tokens
  • Solution: Implement proper token refresh flows; add clear error messages indicating auth failure reason; document token lifecycle

Rate Limit Exceeded

  • Error: API consumer exceeded allowed request rate
  • Solution: Return 429 status with Retry-After header; implement exponential backoff guidance; provide rate limit info in response headers

Database Connection Issues

  • Error: Cannot connect to database or query timeout
  • Solution: Implement connection pooling; add health checks; configure proper timeouts; implement circuit breaker pattern for resilience

Resources

API Development Frameworks

  • Express.js and Fastify for Node.js APIs
  • Flask and FastAPI for Python APIs
  • Spring Boot for Java APIs
  • Gin and Echo for Go APIs

API Standards and Best Practices

  • OpenAPI Specification 3.0+ for API documentation
  • JSON:API specification for RESTful API conventions
  • OAuth 2.0 and OpenID Connect for authentication
  • HTTP/2 and HTTP/3 for performance optimization

Testing and Monitoring Tools

  • Postman and Insomnia for API testing
  • Swagger UI for interactive API documentation
  • Artillery and k6 for load testing
  • Prometheus and Grafana for monitoring

Security Best Practices

  • OWASP API Security Top 10 guidelines
  • JWT best practices for token-based auth
  • Rate limiting strategies to prevent abuse
  • Input validation and sanitization techniques

Quick Install

/plugin add https://github.com/jeremylongshore/claude-code-plugins-plus/tree/main/api-batch-processor

Copy and paste this command in Claude Code to install this skill

GitHub 仓库

jeremylongshore/claude-code-plugins-plus
Path: plugins/api-development/api-batch-processor/skills/api-batch-processor
aiautomationclaude-codedevopsmarketplacemcp

Related Skills

evaluating-llms-harness

Testing

This Claude Skill runs the lm-evaluation-harness to benchmark LLMs across 60+ standardized academic tasks like MMLU and GSM8K. It's designed for developers to compare model quality, track training progress, or report academic results. The tool supports various backends including HuggingFace and vLLM models.

View skill

langchain

Meta

LangChain is a framework for building LLM applications using agents, chains, and RAG pipelines. It supports multiple LLM providers, offers 500+ integrations, and includes features like tool calling and memory management. Use it for rapid prototyping and deploying production systems like chatbots, autonomous agents, and question-answering services.

View skill

huggingface-accelerate

Development

HuggingFace Accelerate provides the simplest API for adding distributed training to PyTorch scripts with just 4 lines of code. It offers a unified interface for multiple distributed training frameworks like DeepSpeed, FSDP, and DDP while handling automatic device placement and mixed precision. This makes it ideal for developers who want to quickly scale their PyTorch training across multiple GPUs or nodes without complex configuration.

View skill

nestjs

Meta

This skill provides NestJS development standards and architectural patterns for building domain-centric applications. It covers modular design, dependency injection, decorator patterns, and key framework features like controllers, services, middleware, and interceptors. Use it when developing NestJS applications, implementing APIs, configuring microservices, or integrating with databases.

View skill