Skill UI

This skill provides implementation patterns for policy guardrails when utilizing Retell AI for voice agents and phone call automation. It is designed to help ensure that AI interactions in a telephony context adhere strictly to predefined business rules and safety policies, preventing unauthorized actions or deviations from the intended conversational flow.

RWKV is an RNN+Transformer hybrid architecture with linear-time inference, infinite context, and GPT-style training. It delivers constant-memory, streaming generation on CUDA and CPU, ideal for long-context, memory-constrained deployments.

This skill specializes in transforming theoretical threat models, risk assessments, and business context into concrete, actionable security requirements. It is essential for writing detailed security user stories, defining robust acceptance criteria, building comprehensive security test cases, and ensuring compliance requirement mapping. Use it when bridging the gap between identifying a vulnerability and specifying how the system must be secured.

This skill provides a comprehensive walkthrough for setting up and testing Sentry error monitoring using both Node.js and Python SDKs. It demonstrates how to capture various event types, including simple messages and full exceptions, and crucially, how to enrich these events with user context, searchable tags, and detailed breadcrumbs. This is essential for verifying robust error logging and monitoring in a production environment.

This comprehensive guide outlines the best practices and structured methodology for developing specialized skills for AI coding agents. It provides detailed instructions on integrating Azure SDKs and Microsoft Foundry services, covering crucial topics like secure authentication patterns (DefaultAzureCredential), modular workflow design, and maintaining concise, effective knowledge context for optimal agent performance.

Provides a structured workflow for defining agent-agnostic subagents with isolated context, covering purpose definition, metadata configuration, prompt writing, and common specialist patterns for verification, debugging, security auditing, and review.

Provides production-ready Megatron-Core workflows for training 2B–462B parameter LLMs with tensor/pipeline/expert/context parallelism, maximizing GPU efficiency on H100 clusters.

This guide provides production-ready patterns for implementing the TwinMind AI memory and meeting intelligence REST API. It covers best practices for authentication, storing contextual memories, searching knowledge bases, and processing meeting transcripts to extract actionable insights. Use this when building robust, scalable AI-powered applications or standardizing team coding practices.

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.

This guide provides advanced, deep-dive debugging techniques for complex issues within the Windsurf integrated development environment. It addresses persistent problems such as context corruption in Cascade, indexing failures, extension conflicts, and deep configuration bugs that standard troubleshooting cannot resolve. Use this when the AI suggestions or IDE behavior is consistently incorrect or unstable.

This skill provides comprehensive guidance on selecting the optimal workspace architecture for Windsurf, adapting to various project scales. Whether managing small solo projects, large monorepos, or complex multi-service, polyglot systems, it outlines strategies for structuring file systems and configuring rules. The goal is to ensure that AI tools receive accurate and focused project context, maximizing developer efficiency.

This skill provides advanced context management for AI in large-scale, complex codebases. It optimizes context windows, ensures memory persistence across multiple sessions, and helps prioritize critical information. Use it when working with massive code repositories to maintain context and improve developer workflow.