AIエージェントの能力を拡張するClaudeスキルの完全なコレクションをご覧ください。
Synchronizes Claude Code and OpenAI Codex CLI workflows by automating documentation translation and providing secure execution wrappers.
Generates real-world evidence from observational data using advanced R-based causal inference and target trial emulation techniques.
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.
Performs comprehensive network meta-analyses in R using frequentist and Bayesian frameworks to compare multiple treatments simultaneously.
Generates insightful, professional-grade charts and interactive dashboards using industry-standard libraries and design principles.
Builds and analyzes robust Bayesian statistical models using Stan-based R packages like brms and rstanarm.
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.
Generates high-fidelity, production-grade frontend interfaces with distinctive aesthetics that transcend generic AI-generated designs.
Streamlines monorepo development by enforcing absolute path execution to prevent directory confusion and shell state errors.
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.
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.
Streamlines MongoDB integration across Node.js, Python, and Java with optimized patterns for transactions, connection pooling, and error handling.
Manages polyglot tool versions and project environments using mise to ensure consistent development workflows and resolve PATH issues.
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.
Implements robust, decision-driven analytics tracking plans and event measurement across marketing and product stacks.
Automates the installation and configuration of the Jira MCP server to enable Claude Code to manage tickets and project workflows.
Resolves incompatible Zod schema errors in Output SDK by redirecting imports from the standard 'zod' package to '@output.ai/core'.
Automates the end-to-end development lifecycle by translating project management tasks and PRDs into technical specifications and production code.
Standardizes the creation of efficient, high-performance tools and MCP interfaces for AI agents.
Scroll for more results...