Skill UI

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.

A comprehensive checklist for deploying large-scale, production-ready GPU workloads on Vast.ai. It covers critical lifecycle stages including account authentication, instance validation, data encryption, persistent checkpointing strategies, fault tolerance for spot instances, robust monitoring (utilization & cost), and strict cost controls, ensuring reliable and stable ML/AI deployments.

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.

Auto-activating assistant for Zapier integration helping with workflow automation, email handling, spreadsheet operations and business process optimization through best-practice guidance and production-ready outputs.

Automates posting AI-focused job ads on Xiaohongshu by generating geek-style cover and detail images, crafting compliant copy, and running Playwright to publish with minimal input.

Medchem Filtering Toolkit is a Python library offering drug-likeness heuristics, structural alert and PAINS filters, group detection, catalogs, and complexity scoring to triage compound libraries and prioritize leads in drug discovery workflows.

This tool guides users through developing a comprehensive content strategy designed to drive organic traffic, build topical authority, and generate qualified leads. It helps define core content pillars, map out topic clusters, and identify high-potential topics by analyzing business goals, customer pain points, and competitive gaps. It ensures content creation is searchable, shareable, and directly aligned with product marketing objectives.

This advanced engine acts as the memory layer for C-suite advisory sessions. It loads, manages, and enriches detailed company context from local files, automatically detecting context staleness. Crucially, it enforces strict anonymization protocols before any external API calls, ensuring sensitive data (like revenue or names) is protected while providing deep, specific insights for high-level business strategy.

This skill provides comprehensive operational controls for long-lived, cloud-hosted agent workloads. It manages the entire lifecycle—from deployment and scaling to observability and security. Features include detailed monitoring (logs, metrics, traces), safety controls (kill switches, least privilege), and robust change management processes (rollout, rollback, auditing). Ideal for mission-critical microservices and background agents.

Guides growth engineers through evaluating, designing, and launching free marketing tools that drive traffic, leads, and backlinks without paid ads, covering idea scoring, UX, lead capture, and launch tactics.

This infrastructure provides a robust solution for running powerful AI agents in remote, sandboxed environments. It overcomes the limitations of local execution by ensuring unlimited concurrency, consistent environments, and collaborative features. Use this to build complex background coding agents, multi-client interfaces (e.g., Slack, Web), or scale agent workloads far beyond local machine constraints.