Skill UI

This skill provides comprehensive management for multiple local Command Line Interface (CLI) agents. It allows users to start, stop, monitor, and assign tasks to these agents, running them in separate tmux sessions for parallel execution. It is ideal for complex, scheduled workflows requiring multi-agent coordination, task monitoring, and log tailing, making it invaluable for advanced automation setups.

A sophisticated hybrid memory system designed specifically for AI agents. It provides a persistent, searchable knowledge bank to store, retrieve, and manage long-term knowledge, including architectural designs, operational patterns, and critical decision points. By syncing with project documentation, it ensures that crucial institutional knowledge remains accessible throughout the development lifecycle, enhancing agent reliability and consistency.

This comprehensive guide details the architecture of memory for intelligent agents. It covers the distinction between short-term context, long-term vector storage, and advanced cognitive models (CoALA framework: semantic, episodic, procedural). Learn how to implement robust memory retrieval mechanisms, select appropriate vector databases (Pinecone, Qdrant, etc.), and utilize embedding models to build truly context-aware, persistent AI applications.

A systematic workflow designed to significantly improve the reliability and performance of existing AI agents. It covers detailed performance analysis, including gathering metrics (e.g., task success rate, latency), classifying failure modes, and advanced prompt engineering techniques (like Chain-of-Thought and Constitutional AI). The process culminates in structured A/B testing and controlled deployment, ensuring measurable, robust improvements.

An advanced AI-driven framework for holistic performance engineering of multi-agent systems. It provides coordinated profiling, workload distribution, and cost-aware orchestration across various domains (database, application, frontend). Use this tool to identify bottlenecks, improve throughput, enhance reliability, and manage resource constraints in complex agent workflows.

This skill defines the coordination protocol for multi-agent execution during development sprints. It governs how project architects must spawn specialized agents (e.g., backend, frontend, QA), manage dependencies via shared contracts, and collect comprehensive, structured reports. It ensures systematic, parallel, and controlled project iteration development.

Covers designing agent tools from schema creation through validation and error handling, emphasizing descriptions that keep LLMs accurate and reusable via MCP standards.

Guide for automating Agentql tasks through Composio’s Rube MCP toolkit, covering prerequisites, setup, discovery, execution, and error avoidance so you can safely run workflows using the latest schemas.

Utilizes the Azure AI Projects SDK's ImageBasedHostedAgentDefinition to build robust, container-based AI agents deployed in Azure AI Foundry. This workflow is essential for creating production-grade agents that require custom logic, precise resource allocation (CPU/Memory), and integration of advanced tools like code interpreters.

Guide to automate Agenty tasks through Composio’s Agenty toolkit by orchestrating Rube MCP workflows, ensuring schema discovery, connection checks, and structured tool execution.

This tool aggregates breaking news from over 50 authoritative cryptocurrency sources using real-time RSS feeds. Users can filter news by specific coins (e.g., BTC, ETH), categories (DeFi, NFT), time periods, or relevance score. It is ideal for market analysis, monitoring major announcements, or tracking sector developments in depth.

Aggregates performance metrics from applications, systems, databases, caches, queues, and external services into a central platform, helping teams define a consistent taxonomy, choose aggregation tools, and create dashboards and alerts for faster monitoring and troubleshooting.