Skill UI

Automates 21risk workflows through Composio's 21risk toolkit via Rube MCP, guiding users to discover tools, verify active connections, and run schema-compliant executions with explicit memory and session management.

Automates ActiveCampaign workflows through Composio’s Rube MCP by discovering tool schemas, checking connections, and executing toolkit operations with enforced memory, schema, and session practices.

AddressSanitizer accelerates fuzz testing by instrumenting C/C++ and unsafe Rust code to detect buffer overflows, use-after-free, and related memory corruption bugs at runtime, with configurable ASAN_OPTIONS for verbosity, abort behavior, and leak control.

Specializes in the expert deployment and orchestration of production-grade multi-agent systems using Google's Agent Development Kit (ADK). This skill manages the complex lifecycle, including AgentCard discovery, A2A protocol communication, state management via Memory Bank, and secure execution within the Code Execution Sandbox. Ideal for setting up robust, enterprise-level AI workflows on the Vertex AI Agent Engine, especially during CI/CD pipelines.

Provision production-grade Vertex AI ADK Agent Engine infrastructure with Terraform, covering Agent Engine runtime, code execution sandbox, Memory Bank, VPC Service Controls, and secure IAM setup for multi-agent deployments.

Guides connecting Rube MCP to Composio’s Adyntel toolkit, searching for live tool schemas, validating connections, and executing multi-tool workflows while handling session reuse, memory, and pagination requirements.

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.

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.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

A comprehensive, multi-phase workflow designed for building sophisticated AI systems. This guide covers everything from designing autonomous agents and implementing single-agent logic, to orchestrating complex multi-agent systems and integrating advanced tools and memory mechanisms. Ideal for developers building production-grade AI applications using frameworks like CrewAI and LangGraph.

This skill covers the comprehensive architecture design and construction of autonomous AI agents. Experts master advanced concepts including tool use, hierarchical memory systems (episodic, semantic, working), sophisticated planning strategies (ReAct, Plan-and-Execute), and multi-agent orchestration. It focuses on building systems that achieve high levels of autonomy while maintaining critical human oversight, designing for graceful degradation and rigorous error handling.