Skill UI

A comprehensive guide to installing and configuring the Vast.ai Command Line Interface (CLI) and REST API access. This skill is used for setting up new cloud integrations, managing API keys, and initializing access to GPU compute instances from the Vast.ai marketplace. It provides steps for CLI authentication, environment variable setup, and implementing a Python client for resource management.

This guide establishes a fast, reproducible local development workflow for GPU workloads using Vast.ai. It covers key practices such as testing Docker images locally (in CPU mode), mocking external APIs like Vast.ai, and implementing robust unit and integration tests. This is essential for minimizing cloud GPU costs and accelerating the iteration cycle for complex AI/ML projects.

This deep dive guides users through migrating complex GPU workloads, such as deep learning training pipelines, from major hyperscalers (AWS, GCP, Azure) or other cloud platforms to Vast.ai. It covers critical steps including cost comparison analysis, adapting Docker images for new environments, securely handling cloud credentials via environment variables, and implementing robust validation and rollback procedures to ensure a smooth transition.

This comprehensive guide outlines security best practices for utilizing Vast.ai's cloud environment. It covers secure API key management, hardening SSH access to GPU instances, implementing proper data cleanup before instance destruction, and adhering to robust network security protocols. Use this when setting up, auditing, or deploying sensitive AI/ML workloads on shared cloud hardware.

Expert capability for designing and implementing complex semantic search systems using vector databases. Proficient in embedding strategies, index optimization (HNSW, IVF), and advanced retrieval methods like RAG and hybrid search. Masters major platforms including Pinecone, Weaviate, Qdrant, Milvus, and pgvector for building sophisticated recommendation and similarity search applications.

This comprehensive guide outlines best practices for optimizing vector indexes for production systems. It covers critical techniques such as tuning HNSW parameters, implementing quantization strategies, and optimizing memory usage. Use this when you need to balance search latency, recall, and scalability for large-scale vector databases.

Configures robust CI/CD pipelines for Vercel projects using GitHub Actions. This guide automates the full deployment lifecycle, including generating preview environments on every pull request, performing reliable production builds upon merging to main, and implementing test gating to ensure code quality before release. Ideal for professional web development workflows.

A comprehensive guide for developers on handling sensitive data and ensuring compliance with major privacy regulations like GDPR and CCPA when deploying serverless functions on Vercel. Learn techniques for PII redaction in logs, implementing data subject rights (Right to Access/Erasure), securing session cookies, and configuring data residency to meet global privacy standards.

Configure Vercel's advanced role-based access control (RBAC) for enterprise environments. This guide covers setting up granular team roles, managing project-level access groups, integrating SAML SSO for secure authentication, and enabling detailed audit logging. Use this when implementing strict security protocols and enforcing the principle of least privilege across your team's deployments and resources.

This guide details how to set up a full local development loop for applications deployed to Vercel. It covers using `vercel dev` to emulate serverless functions, managing environment variables, and implementing hot reloading. It provides step-by-step instructions for local testing of API routes and frontends, ensuring a fast and accurate iteration cycle before production deployment.

This comprehensive guide details the process of migrating applications to Vercel from various platforms, including Netlify, AWS, and Cloudflare. It covers critical steps such as configuration mapping (e.g., netlify.toml to vercel.json), migrating serverless functions, setting up environment variables, implementing DNS cutovers, and validating feature parity using advanced techniques like the Strangler Fig pattern, ensuring a smooth and reliable transition.

A comprehensive guide to configuring Vercel across Development, Preview, and Production environments. Learn how to scope environment variables, manage secrets, and automate multi-stage deployments using both the Vercel CLI and REST API. Covers setting up custom environments (e.g., Staging) and implementing environment detection in code for reliable application behavior.