Skill UI

Automatically checks application metrics such as load times, bundle sizes, and API responses against predefined performance budgets, surfacing regressions and preventing slippage before deployments—ideal for CI/CD workflows.

This expert skill validates the deployment readiness of Vertex AI Agent Engines. It executes a weighted audit across five critical dimensions: security, monitoring, performance, compliance, and best practices. The output provides a comprehensive 0-100% composite score, a detailed pass/fail report for each check, and a prioritized, actionable remediation plan, ensuring high-quality, production-grade deployments.

This skill provides robust guidelines and patterns for managing environment variables and sensitive credentials securely. It is designed to ensure that secrets, API keys, and other confidential information are never accidentally exposed or logged in Claude sessions, terminal outputs, development logs, or version control commits (like Git). Use this skill when handling sensitive data in development workflows to maintain maximum security.

This guide details how to integrate Vast.ai's GPU provisioning capabilities into automated CI/CD pipelines, such as GitHub Actions. It enables developers to run GPU-accelerated tests, model benchmarks, and validations automatically. Key features include robust instance lifecycle management (provisioning, running, and cleanup) and advanced cost-control mechanisms (e.g., setting budget limits) to ensure reliable and efficient testing in a cloud environment.

A comprehensive guide for diagnosing and resolving common errors encountered when utilizing the Vast.ai platform. It provides quick references and fixes for HTTP status codes (e.g., 401, 429), instance lifecycle issues, CLI command failures, Docker image loading problems, SSH connectivity errors, and CUDA memory issues. Essential for maximizing GPU rental efficiency and debugging cloud instances.

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.

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 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.