Skill UI

Generates complete, production-ready Google Cloud Platform (GCP) code examples and infrastructure templates. It spans various modern AI patterns, including ADK agents, Genkit RAG flows, and multimodal analysis using Vertex AI and Gemini. The output includes runnable code, deployment configuration (Terraform/IaC), security best practices (IAM, VPC-SC), and monitoring setup, ensuring enterprise-grade reliability and cost optimization.

A comprehensive guide to building enterprise-grade AI applications using Firebase Genkit. This skill covers the full development lifecycle, including implementing advanced RAG systems, multi-step tool-calling agents, and complex workflows across Node.js, Python, and Go. It teaches best practices for schema validation (Zod/Pydantic), robust error handling, OpenTelemetry tracing, and deploying reliably to Firebase Functions or Cloud Run.

Master Google Cloud’s Agent Development Kit and Starter Pack to build containerized multi-agent systems, automate RAG workflows, and deploy production-ready agents via Cloud Run, GKE, Agent Engine, or CI/CD pipelines.

Execute complex and compute-intensive workloads directly on fully managed Hugging Face cloud infrastructure. This skill eliminates the need for local setup, allowing users to run jobs using cloud CPUs, GPUs, or TPUs. Ideal for large-scale data processing, batch inference, reproducible model training, and scheduled automation tasks, while persisting results to the Hugging Face Hub.

Playbook for running ML training and inference on Lambda Labs’ on-demand GPU cloud, covering when to choose it, console/SSH/API workflows, available GPUs, pricing, and persistent storage with Lambda Stack pre-installed.

This guide provides comprehensive instructions for deploying applications powered by the Perplexity Sonar API. It covers setup for major cloud platforms like Vercel, Google Cloud Run, and Docker. Users can deploy production-ready endpoints, manage required secrets, implement edge functions, and utilize caching mechanisms (e.g., Redis) to ensure high availability and performance in production environments.

This comprehensive workflow guides users through the end-to-end process of utilizing cloud-based GPU computing resources on Vast.ai. It covers searching for ideal GPU offers based on specs and price, provisioning an instance, running complex training or inference jobs via SSH, collecting model artifacts, and crucially, destroying the instance to prevent unexpected billing. Ideal for deep learning training and research.

A comprehensive guide demonstrating the full lifecycle of GPU resource utilization on Vast.ai. This includes searching for available hardware using CLI and REST APIs, provisioning a dedicated GPU instance (e.g., using PyTorch images), connecting via SSH, running deep learning workloads, and finally, securely destroying the instance to stop billing. Ideal for testing cloud integration or managing ML compute pipelines.

Scaffolds production-ready generative AI agents on Vertex AI with Gemini models, multi-modal tools, RAG, and Cloud Run/Endpoint deployment, including observability and evaluation for enterprise scenarios.

Train or fine-tune TRL-powered language models via Hugging Face Jobs, running SFT/DPO/GRPO workflows on managed cloud GPUs, auto-saving to the Hub and exporting GGUF builds without local setup.

A zero-config, serverless cloud platform designed for running intensive GPU workloads, including model training, fine-tuning, and inference. It eliminates the need for complex infrastructure setup (no SSH, no Docker) while supporting local-first development and auto-scaling for various high-performance GPUs (A100, H100). Ideal for data scientists and ML engineers requiring scalable and cost-efficient remote compute resources.

Modal is a powerful serverless cloud platform designed specifically for running AI and Machine Learning workloads. It allows users to deploy, serve, and scale complex models (like LLMs) without managing infrastructure. Key features include on-demand access to high-end GPUs (A100, H100), serverless function scaling, persistent storage, and the ability to define entire infrastructure using pure Python code, eliminating the need for separate YAML or Dockerfiles.