Skill UI

Modal Serverless GPU provides on‑demand, pay‑per‑second access to T4 through H200 GPUs, letting you deploy ML models as auto‑scaling APIs, run batch jobs, and prototype without managing infrastructure.

Fully managed, auto-scaling vector database tailored for production AI workloads such as RAG, recommendations, and semantic search, offering hybrid dense+sparse search, namespaces, metadata filters, and <100ms latency on serverless infrastructure.

Automates SNS topic configuration tasks within AWS, offering guidance, best practices, code generation, and validation for compute, storage, networking, and serverless scenarios.

Modal delivers cloud-native serverless Python with on-demand GPUs, persistent storage, and web endpoints to deploy ML workloads, batch jobs, and APIs without writing Dockerfiles.

Modal is a serverless cloud platform designed for executing Python-based AI/ML workloads. It provides on-demand access to powerful GPUs (e.g., H100, A100), scalable serverless functions, and custom containerization. Use cases include deploying ML models, running large-scale training/inference jobs, creating web APIs, and scheduling data pipelines, all defined entirely in code.

This skill provides comprehensive guides for deploying stateless, serverless API wrappers powered by Claude (Anthropic). It details how to integrate and stream responses from the Messages API across major cloud environments, including Vercel Edge Functions, Fly.io, and Google Cloud Run. It is essential for productionizing AI applications that require robust secret management, scalable infrastructure, and real-time streaming capabilities.

This guide provides comprehensive instructions and code examples for deploying ElevenLabs Text-to-Speech (TTS) applications to various cloud environments, including Vercel (serverless), Fly.io (containers), and Google Cloud Run. It covers best practices for managing platform-specific secrets, configuring timeouts, and implementing real-time streaming audio capabilities for production-grade voice applications.

Offensive cloud security methodology for AWS, Azure, and GCP. Covers identity discovery, credential harvesting, enumeration, privilege escalation, persistence, data exfiltration, lateral movement, serverless abuse, Kubernetes paths, and detection evasion during authorized assessments.