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Manage GPU Instances and ML Workflows

vastai-core-workflow-a

jeremylongshore/claude-code-plugins-plus-skills

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.

Vast.ai Distributed Training and Cost Management

vastai-core-workflow-b

jeremylongshore/claude-code-plugins-plus-skills

This advanced workflow orchestrates complex, multi-node GPU clusters on Vast.ai. It is designed for large-scale distributed machine learning training, automatically handling spot instance preemption and resuming jobs from checkpoints. Furthermore, it provides comprehensive cost analysis by tracking billing history, allowing users to optimize GPU spending and manage cluster teardown efficiently.

Local Dev Loop for GPU Workloads

vastai-local-dev-loop

jeremylongshore/claude-code-plugins-plus-skills

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.

Advanced GPU Performance Tuning for AI

vastai-performance-tuning

jeremylongshore/claude-code-plugins-plus-skills

A comprehensive guide to optimizing deep learning workflows and maximizing efficiency on cloud GPU platforms like Vast.ai. Learn techniques for selecting optimal instances by performance-per-dollar, reducing startup latency, tuning data pipelines (DataLoader, pin_memory), and scaling multi-GPU training using PyTorch and Bash scripting.

Vast.ai GPU Compute Workflow Architecture

vastai-reference-architecture

jeremylongshore/claude-code-plugins-plus-skills

This guide details the production-grade reference architecture for running complex GPU compute workflows on Vast.ai. It covers the foundational three-tier pattern—Orchestrator, GPU Workers, and Artifact Storage—providing blueprints for job queue management, fault-tolerant training pipelines, and robust checkpointing. Use this when designing scalable and reliable ML/AI training systems.

Vast.ai Cloud SDK Best Practices

vastai-sdk-patterns

jeremylongshore/claude-code-plugins-plus-skills

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.

Building Event-Driven GPU Workflows

vastai-webhooks-events

jeremylongshore/claude-code-plugins-plus-skills

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.

Venly Automation Flow

venly-automation

ComposioHQ/awesome-claude-skills

Automate Venly operations via the Composio Rube MCP stack by discovering current tool schemas, validating connections, and executing schema-compliant actions for NFT and wallet workflows.

Veo Automation Toolkit

ComposioHQ/awesome-claude-skills

Automate Veo tasks by orchestrating Composio’s toolkit through Rube MCP, following mandatory tool discovery, connection checks, and schema-compliant executions before running workflows.

Vercel Automation Toolkit

vercel-automation

davepoon/buildwithclaude

Automates Vercel operations through Composio’s Rube MCP toolkit, covering deployment inspections, creation, env var updates, DNS, and project configuration via guided workflows.

Vercel CI/CD Deployment with GitHub Actions

vercel-ci-integration

jeremylongshore/claude-code-plugins-plus-skills

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.

Vercel Deploy Claimable

vercel-deploy-claimable

sickn33/antigravity-awesome-skills

Enables teams to identify and manage claimable Vercel deployments, helping developers take ownership of preview builds or stalled sites in a CI/CD workflow.

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