integration▌

Reference: docs.dodopayments.com/developer-resources/integration-guide

Patterns for creating high-performance AWS Lambda functions in TypeScript with optimized cold starts.

Patterns for creating high-performance AWS Lambda functions in Python with optimized cold starts and clean architecture.

Add a new framework integration to evlog. Every integration follows the same architecture built on the shared createMiddlewareLogger utility. This skill walks through all touchpoints. Every single touchpoint is mandatory -- do not skip any.

Reference: docs.dodopayments.com/developer-resources/webhooks

Integrate with Apple ecosystem for iOS and macOS automation.

Use this skill when the user needs integration architecture and runtime plumbing: Named Credentials, External Credentials, External Services, REST/SOAP callout patterns, Platform Events, CDC, and event-driven integration design.

Production-ready AWS RDS configuration patterns for Spring Boot applications with Aurora, MySQL, and PostgreSQL. \n \n Supports Aurora MySQL, Aurora PostgreSQL, and standard MySQL/PostgreSQL with datasource configuration, HikariCP connection pooling, and SSL encryption \n Includes environment-specific profiles (dev/prod), Flyway database migrations, and read/write endpoint splitting for read-heavy workloads \n Provides security patterns using environment variables and AWS Secrets Manager integra

Production-ready patterns for authenticating, transforming, and reliably integrating third-party APIs. \n \n Covers authentication methods including API keys, OAuth 2.0, and JWT with secure credential management \n Provides structured error handling, exponential backoff retry logic, and client-side rate limiting with configurable windows \n Includes webhook verification via HMAC signatures, request/response transformation, and pagination handling patterns \n Demonstrates REST client patterns, ti

Spring Boot auto-configuration and declarative AI services for LangChain4j integration. \n \n Provides property-based configuration for multiple AI providers (OpenAI, Azure, Ollama) with Spring Boot starters and automatic bean wiring \n Enables interface-based AI service definitions using @AiService annotations combined with message templates and Spring dependency injection \n Supports RAG systems through configurable embedding stores (pgvector, Neo4j, Pinecone) and document ingestion pipelines