Explore our collection of Agent Skills to enhance your AI workflow.
Simplifies Amazon DynamoDB integration in Java applications using the AWS SDK v2 and Enhanced Client patterns.
Implements secure encryption, key management, and digital signatures using the AWS SDK for Java 2.x.
Integrates LangChain4j with Spring Boot to build production-ready AI applications using declarative services and automated configuration.
Streamlines the creation of lightweight unit tests for Jakarta Bean Validation constraints and custom validators without requiring a Spring context.
Configures production-grade observability, health monitoring, and secure management endpoints for Spring Boot applications.
Provisions production-ready Google Kubernetes Engine clusters using optimized templates and Google Cloud best practices.
Automates the multi-step process of adding and configuring Tabler icons within the Arto project's Rust and Vite architecture.
Configures and optimizes AWS SDK for Java 2.x service clients with industry-standard patterns for authentication, performance, and resource management.
Simplifies the integration of large language models into Java applications using declarative interfaces and annotations.
Automates the generation of comprehensive OpenAPI 3.0 specifications and interactive Swagger UI for Spring Boot 3.x applications.
Provides research-backed startup advice by accessing Y Combinator’s library of essays, founder interviews, and startup school lectures.
Streamlines Rust application development by integrating standardized error handling, testing, and logging patterns through a modular composition system.
Generates comprehensive unit tests for Spring Boot REST controllers using MockMvc and Mockito to validate API endpoints in isolation.
Streamlines graph database development by providing standardized patterns for Spring Data Neo4j entity mapping, Cypher queries, and repository implementation.
Streamlines the process of adding new AI agent cost and token usage trackers to the Splitrail monitoring system.
Conducts rigorous, adversarial code reviews across multiple languages to identify security vulnerabilities, performance bottlenecks, and poor coding patterns.
Generates comprehensive architectural documentation and Mermaid diagrams by researching and mapping existing codebases.
Manages and offloads agent context to the filesystem to overcome context window limits and improve token efficiency.
Optimizes development workflows by enabling naming, checkpointing, and resuming of persistent Claude Code sessions.
Optimizes how AI agents interact with external systems through professional tool design principles, consolidation strategies, and MCP standards.
Establishes robust frameworks for measuring, testing, and optimizing AI agent performance through multi-dimensional rubrics and LLM-as-judge methodologies.
Design and scale remote, sandboxed execution environments for autonomous and collaborative background agents.
Automates the creation of detailed GitHub issues for local integration test failures by analyzing logs and agent metadata.
Provides architectural principles and best practices for building robust Model Context Protocol (MCP) servers.
Streamlines web application testing with comprehensive E2E strategies, Playwright automation, and systematic deep audit workflows.
Transforms external RDF context into formal Belief-Desire-Intention (BDI) mental states for advanced cognitive agent reasoning.
Builds SwiftUI views and captures simulator screenshots for real-time visual UI analysis and debugging within Claude.
Captures and analyzes SwiftUI previews and iOS simulator screenshots directly within your development workflow.
Transforms project briefs into structured, testable specifications using a spec-driven development methodology.
Reduces Claude Code skill file size and token usage through systematic externalization and modularization.
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