55 boosters for "infrastructure" — open source, verified from GitHub, ready to install
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.
Performance Benchmarker is a specialized agent that helps developers measure, analyze, and optimize system performance across applications and infrastructure. It's ideal for teams needing data-driven insights into bottlenecks and performance improvements.
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.
Infrastructure Maintainer is an expert agent that helps teams design, monitor, and optimize cloud infrastructure for reliability, performance, and cost efficiency. DevOps engineers, platform teams, and SREs can use it to troubleshoot systems, plan scaling strategies, and maintain high availability.
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.
Upstash QStash expert booster enables developers to build serverless message queues, scheduled jobs, and reliable HTTP-based task delivery without infrastructure management. Ideal for AI coding assistants helping teams implement async processing, cron jobs, and webhook systems.
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.
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:
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
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.
This MCP server enables Claude to interact directly with Kubernetes clusters through kubectl commands, allowing developers to manage, inspect, and troubleshoot K8s deployments conversationally. It's essential for DevOps engineers and platform teams who want AI-assisted Kubernetes management.
"version": "0.25.0", "description": "Datadog API CLI with 49 command groups, 300+ subcommands. Skills and domain agents for monitoring, logs, APM, security, and infrastructure.", "email": "support@datadoghq.com"
Switch between Docker and Kubernetes backends for Kurtosis. After switching, restart the engine: When using Kubernetes:
This Cursor rule guides developers to use Bun as their default runtime and build tool, replacing Node.js, npm, and common libraries with Bun's native equivalents. Ideal for agents and indie developers building with Cursor who want to standardize on Bun's faster, integrated toolchain.
In Memoria is a persistent intelligence infrastructure MCP server that enables AI agents and developers to build codebase-aware applications with semantic search and pattern learning capabilities. It's designed for developers building AI-powered tools and agents that need long-term memory and code understanding.
"name": "@cyanheads/mcp-ts-core", "version": "0.2.11", "mcpName": "io.github.cyanheads/mcp-ts-core",
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 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.
Terraform and OpenTofu infrastructure-as-code expert that guides module design, state management, multi-environment setups, and CI/CD integration. Essential for DevOps engineers and platform teams building scalable cloud infrastructure.
Persistent local memory for AI agents. Save, recall, and search project decisions as local JSON. Zero cloud, zero infrastructure.
You are a DevOps and infrastructure engineer specializing in Solana project deployment and operations. You build reliable CI/CD pipelines, manage RPC infrastructure, configure monitoring, and deploy edge services. You prioritize reproducible builds, secure secret management, and observable systems.
