67 boosters for "cloud" — open source, verified from GitHub, ready to install
A specialized agent that guides users through designing, building, and operating scalable data pipelines and lakehouse architectures. Data engineers, analytics engineers, and platform teams use this to architect reliable ETL/ELT systems and cloud data infrastructure.
Backend Architect is an AI agent that provides expert guidance on scalable system design, database architecture, API development, and cloud infrastructure. It's ideal for developers and teams building robust, secure server-side applications and microservices.
A specialized security expert agent that performs threat modeling, vulnerability assessment, and secure code review to help developers build secure applications and cloud infrastructure. Ideal for security-conscious development teams and engineers seeking expert-level security guidance.
DevOps Automator is an expert agent that automates infrastructure, CI/CD pipelines, and cloud operations to help engineering teams reduce manual toil and ship faster. It's ideal for teams seeking to streamline deployments and improve system reliability.
Streamlines Cloudflare deployments by guiding users through Workers, Pages, and platform services with decision trees and authentication verification. Developers building on Cloudflare benefit from consolidated, quick-start deployment guidance.
render-deploy automates deployment to Render's cloud platform by analyzing codebases and generating render.yaml Blueprints with Dashboard deeplinks. Developers building applications on Render will find this essential for streamlined deployments.
Train object detection, image classification, and SAM/SAM2 segmentation models on managed cloud GPUs. No local GPU setup required—results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Helper scripts use PEP 723 inline dependencies. Run them with :
Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub. Use this skill when users want to: Use Unsloth () instead of standard
Run any workload on fully managed Hugging Face infrastructure. No local setup required—jobs run on cloud CPUs, GPUs, or TPUs and can persist results to the Hugging Face Hub. Use this skill when users want to: When assisting with jobs:
Provides the Hugging Face Hub CLI (`hf`) tool for downloading, uploading, and managing models, datasets, and Spaces directly from Claude Code. Essential for developers integrating Hugging Face resources into AI workflows.
This skill enables users to run Python workloads, Docker jobs, and GPU-intensive tasks on Hugging Face's managed infrastructure without local setup. It's valuable for ML engineers, data scientists, and developers needing cloud compute for training, inference, and batch processing.
A skill for fine-tuning and training language models on Hugging Face's cloud GPU infrastructure using TRL, supporting SFT, DPO, GRPO methods and GGUF conversion for local deployment. Developers and ML engineers working with cloud-based model training benefit from this comprehensive guidance.
Enables developers to interact with Hugging Face Hub directly from Claude Code using the `hf` CLI—downloading models/datasets, uploading files, creating repositories, and managing cache without leaving the coding environment.
Heuristic scoring (no AI key configured).
Retrieve current documentation and code examples for any library using the Context7 CLI. Make sure the CLI is up to date before running commands: Or run directly without installing:
This rule provides a guide to understanding the structure and core functionalities of the MCP Boilerplate project. It's designed to help you navigate and extend the boilerplate effectively. The project is a Cloudflare Worker that implements an MCP (Model Context Protocol) server with built-in suppor
This booster enables developers to manage Alibaba Cloud CDN operations—including domain onboarding, cache management, and certificate updates—directly through an AI coding assistant with OpenAPI/SDK integration. It's valuable for teams building or maintaining CDN infrastructure on Alibaba Cloud.
A Cursor IDE rules configuration for the pig-ui framework that enforces MCP feedback loops during development workflows. Beneficial for teams using Spring Boot 3.5, Spring Cloud, and Vue with role-based access control requirements.
Recall MCP Server enables persistent, cross-session memory for Claude and AI agents through Redis/Valkey or managed cloud hosting, allowing AI systems to retain and retrieve context across conversations. Developers building Claude applications, multi-turn agents, and AI systems requiring long-term context management benefit most from this solution.
"name": "@cyanheads/mcp-ts-core", "version": "0.2.11", "mcpName": "io.github.cyanheads/mcp-ts-core",
A practical guide for deploying serverless Python applications on Modal, enabling developers to run GPU-accelerated AI/ML workloads, web APIs, and batch jobs with minimal infrastructure configuration.
This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, and model persistence. Should be invoked for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
A Cursor AI rules template for building full-stack React 19 + Hono.js applications with SSR, Tailwind CSS, and Shadcn UI on Cloudflare Pages. Ideal for developers seeking an opinionated, production-ready stack with AI-assisted development guidance.
