Skill UI

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.

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 workflow guides users through a multi-phase process for assessing web application security. It is designed to systematically test for common vulnerabilities listed in the OWASP Top 10, including SQL injection, XSS, broken authentication, and access control issues. Ideal for professional penetration testing, security auditing, and bug bounty hunting.

Automatically generates two complementary onboarding documents for any codebase: a Principal-level guide focusing on design philosophy, technical debt, and architectural 'why'; and a Zero-to-Hero guide providing step-by-step setup, development workflows, and practical 'how-to' instructions. Ideal for new team members or documenting complex systems.

This skill simulates an expert software engineer and systems analyst. It is designed for deep, evidence-based understanding of complex codebases. Users can ask "how does X work" or request architectural reviews, ensuring every claim is grounded in actual code paths, data flows, and dependency chains, following a rigorous multi-iteration process.

A comprehensive, multi-phase checklist designed for the systematic rollout of AI coding assistants (like Windsurf) to development teams or entire enterprises. It covers critical areas including workspace configuration, security hardening (SSO/SAML, secret management), establishing team AI policies, structured developer onboarding, key performance indicator (KPI) monitoring, and emergency rollback procedures.

Master the architecture of reliable distributed systems using advanced workflow orchestration patterns. This guide covers fundamental design decisions, including the distinction between Workflows and Activities, and introduces critical patterns like Saga for distributed transactions, Entity Workflows, and Fan-Out/Fan-In. Learn best practices for handling long-running processes, state persistence, and failure recovery to build robust business logic.

A comprehensive directory designed for discovering, researching, and comparing autonomous AI agents, frameworks, and tools across diverse ecosystems. This skill helps users map the current AI agent landscape, identify existing solutions, gather best practices, and pinpoint market gaps before initiating custom development or workflow design.

Generates high-fidelity mobile and web UI/UX mockups with atomic design principles, responsive layouts, and accessible design tokens via muapi.ai for product requirements that need professional wireframes or design systems.

This skill guides users through a comprehensive, multi-stage process for creating high-quality documentation. It systematically moves through Context Gathering (understanding the background and audience), Refinement & Structure (iteratively building and editing sections), and Reader Testing (simulating external review to catch blind spots). Ideal for drafting complex specifications, proposals, or design documents that need to be robust and clear for a wide audience.

PufferLib is a high-performance library designed for advanced reinforcement learning simulations and training. It excels in parallel environment simulation, offering optimized vectorization and native support for multi-agent systems. Utilizing algorithms like PuffeRL (optimized PPO+LSTM), it achieves millions of steps per second, significantly accelerating research and deployment across single-agent and multi-agent environments.

SimPy is a powerful, process-based discrete-event simulation framework written in Python. It is designed for modeling complex systems where entities interact with shared resources and time-based events occur, such as manufacturing lines, service queues, network traffic, or logistics networks. Use this skill to analyze resource contention, simulate process optimization, conduct capacity planning, and model dynamic system behaviors.