AI 에이전트 기능을 확장하는 Claude 스킬의 전체 컬렉션을 살펴보세요.
Optimizes SQL queries and database performance through advanced indexing strategies and execution plan analysis.
Optimizes multi-agent workflows by intelligently delegating specialized tasks to the most effective AI models within the AgentMaestro ecosystem.
Design, simulate, and analyze complex adaptive clinical trials using industry-standard R packages and Bayesian methods.
Provides a standardized blueprint for creating custom Claude Code skills to extend agent capabilities and domain-specific logic.
Streamlines the creation of high-quality technical documentation and proposals through a structured, collaborative co-authoring workflow.
Implements robust unit, integration, and end-to-end testing strategies for JavaScript and TypeScript applications.
Performs comprehensive network meta-analyses in R using frequentist and Bayesian frameworks to compare multiple treatments simultaneously.
Builds and analyzes robust Bayesian statistical models using Stan-based R packages like brms and rstanarm.
Implements rigorous Test-Driven Development (TDD) practices, covering edge cases and ensuring high code coverage through maintainable, fast-executing test suites.
Generates diverse, structured ideas for brainstorming sessions with fine-grained control over creativity levels and output volume.
Refines and documents structured AI execution plans with mandatory validation loops and TDD-first requirements.
Synthesizes patient-level data across multiple studies using advanced meta-analysis methods and R statistical frameworks.
Streamlines monorepo development by enforcing absolute path execution to prevent directory confusion and shell state errors.
Implements comprehensive Python testing strategies using pytest, fixtures, and mocking to ensure code reliability and maintainability.
Streamlines the deployment and management of Azure App Services, Functions, and enterprise-grade cloud environments.
Enforces high-quality, maintainable code through industry best practices, design patterns, and architectural principles.
Enforces disciplined Git workflows by managing specific file staging, structured commit messaging, and automated hook troubleshooting.
Manages GitHub pull requests by listing PRs, conducting comprehensive code reviews, and automating inline feedback.
Enhances AI coding workflows with persistent pattern discovery, multi-phase task tracking, and continuous learning through a specialized memory layer.
Eliminates flaky tests and race conditions by replacing arbitrary timeouts with smart condition-based polling.
Standardizes AI agent development through reusable, composable, and version-controlled prompt templates.
Optimizes machine learning hyperparameters using the R Tidymodels ecosystem for improved model performance.
Automates GitHub pull request creation and management with a focus on material impact and reviewer clarity.
Enforces Ruby coding standards and automatically fixes style violations using the industry-standard RuboCop linter.
Implements robust model validation and resampling techniques using the R tidymodels ecosystem and rsample package.
Manages polyglot tool versions and project environments using mise to ensure consistent development workflows and resolve PATH issues.
Simulates Go-style channel communication patterns using filesystem-based scripts for testing and demonstration purposes.
Implements robust offline data persistence, synchronization strategies, and conflict resolution for resilient mobile applications.
Optimizes AI context window efficiency by organizing large AGENTS.md files into modular, nested structures.
Automates the installation and configuration of the Jira MCP server to enable Claude Code to manage tickets and project workflows.
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