Skill UI

Collects comprehensive diagnostic evidence for troubleshooting Vast.ai GPU instances. This bundle gathers account status, lists all running instances, retrieves recent logs, performs remote GPU diagnostics (e.g., nvidia-smi, CUDA checks), and verifies API connectivity. It is essential for creating detailed support tickets when encountering persistent infrastructure issues.

This guide demonstrates how to implement robust Role-Based Access Control (RBAC) and financial governance for cloud GPU environments. It allows organizations to allocate resources, set team-specific budgets, whitelist approved GPUs, and track spending via detailed audit logs. Ideal for multi-team research and engineering environments requiring strict cost accountability and access separation.

This comprehensive runbook provides rapid, structured procedures for incident response on Vast.ai cloud GPU instances. It covers common failure scenarios like spot preemption, training job crashes, and GPU hardware issues. Users can follow the steps for triage, mitigation, recovery, and postmortem analysis to minimize downtime and ensure computational continuity.

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 guide details how to implement environment isolation (dev, staging, prod) for GPU cloud deployments on Vast.ai. It uses environment variables and configuration classes to enforce separate API keys, budget caps, instance limits, and GPU whitelists for each deployment tier, ensuring efficient resource management and cost control.

A collection of production-ready Python patterns for interacting with the Vast.ai cloud API. This includes typed search query builders, context-managed instance lifecycle (guaranteeing auto-cleanup), offer scoring algorithms (cost, reliability, performance), robust retry mechanisms with backoff, and secure SSH command execution. Essential for building reliable, complex cloud workflow applications.

Implement robust, event-driven workflows for managing GPU resources on Vast.ai. Since native webhooks are unavailable, this skill uses Python polling to continuously monitor instance status changes (e.g., loading, running, exited). It enables complex automation tasks such as auto-recovering from spot preemption, handling lifecycle events, and tracking billing costs.

This script automates the collection of comprehensive diagnostic evidence for Vercel deployments. It gathers critical information including deployment state, recent function logs, environment variables, build outputs, and system details into a single compressed bundle. It is essential for preparing detailed support tickets or performing deep-dive troubleshooting when encountering persistent Vercel issues.

A comprehensive, step-by-step guide for responding to Vercel-related production outages, deployment failures, and platform issues. This runbook covers rapid triage using CLI commands, executing instant rollbacks, investigating root causes (log analysis, diffing), managing communication templates, and conducting thorough postmortem reviews.

This comprehensive checklist is designed for ensuring flawless deployments to Vercel's production environment. It guides users through every critical stage: pre-deployment verification (environment variables, testing), secure promotion and API deployment, domain configuration, post-deployment health checks, and immediate rollback procedures. Use this guide whenever preparing for a major launch or implementing go-live procedures to minimize production risks.

A comprehensive guide for developers to measure, analyze, and optimize website and web application performance. Covers critical areas including Core Web Vitals (LCP, FID, CLS), reducing JavaScript bundle size, implementing effective caching, and optimizing rendering paths to boost user experience and SEO rankings.