Skill UI

Comprehensive expert for deploying and managing AI agents at scale using AWS Bedrock AgentCore. Covers all nine core services including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, and Evaluations. Use for agent deployment, MCP integration, credential management, and cross-service workflows with AWS CLI.

This skill covers deploying and managing Azure Virtual Machines using Azure CLI. It includes creating Linux and Windows VMs, configuring availability sets, managing managed disks, and generating custom images. It also provides guidance on selecting appropriate VM sizes for different workloads like general purpose, memory-intensive, or GPU tasks. Suitable for DevOps engineers and cloud administrators managing compute resources on Azure.

This skill provides comprehensive guidelines for writing idiomatic and efficient C code. It covers critical topics such as memory management, pointer usage, error handling, string manipulation, and struct definitions. The guide emphasizes safety practices like checking malloc returns, avoiding buffer overflows, and using proper error codes. It also highlights common anti-patterns to avoid, ensuring robust and maintainable software development in the C language.

This guide outlines the routing and usage rules for Cherry Studio's first-party MCP tools. It covers capabilities like web search, knowledge management, memory recall, scheduling, CLI execution, and skill installation. It emphasizes respecting mutation boundaries, checking tool availability, and following approval workflows before executing actions.

Elite AI context engineering specialist mastering dynamic context management, vector databases, knowledge graphs, and intelligent memory systems. Provides best practices for orchestrating complex AI workflows, optimizing context windows, and integrating enterprise knowledge bases.

This skill provides comprehensive guidelines for writing idiomatic and efficient C++ code. It covers memory management, modern types, move semantics, templates, error handling, concurrency, and common anti-patterns to ensure safe and maintainable software development.

Maintain durable feature-level memory across AI coding sessions using lightweight Markdown tracks. It organizes status, source-of-truth docs, decisions, risks, and changes for long-lived features. Helps AI agents resume work accurately without relying on chat history. Ideal for repositories needing structured project memory without reorganizing all documentation.

This skill enables building production-ready serverless applications on Google Cloud Platform using Cloud Run. It covers containerized services, event-driven functions, cold start optimization, and Pub/Sub integration. Includes guidance on memory management, concurrency settings, and scaling bottlenecks to ensure efficient deployment and cost control.

Master the LangChain framework to build sophisticated LLM applications. Learn to implement agents, chains, memory management, and tool integration. This skill covers core concepts like ReAct agents, retrieval chains, and document processing pipelines. Ideal for creating autonomous AI agents and production-grade applications using Python.

Provides cross-language patterns for developing memory-safe systems code. This skill covers crucial concepts like RAII, ownership models, smart pointers, and robust resource management. It is essential for preventing common vulnerabilities such as use-after-free, memory leaks, and ensuring reliable resource handling across complex systems.

This skill manages and stores approved team context securely using RuVector isolation. It ensures tenant-local data privacy by excluding sensitive credentials and personal information. During retrieval, it preserves provenance and treats data as untrusted. It prevents memory transfer between teams, maintaining strict security boundaries for AI team operations.