Skill UI

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.

Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.

Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability.

Defines the Sprint plugin's SPAWN REQUEST-driven coordination protocol for spawning implementation, testing, and parallel agents, ensuring scoped work, shared contracts, structured reports, and consistent UI test schema for sprint execution.

This guide details how to use the Azure AI Projects SDK to programmatically define and deploy sophisticated, container-based hosted agents. It covers essential steps including setting up prerequisites, managing container images, specifying resource limits (CPU/memory), and integrating various tools like code interpreters, enabling robust AI agent deployment in Azure AI Foundry.

Aggregates breaking cryptocurrency headlines from 50+ authoritative RSS feeds, supports coin/category/time filters, relevance scoring, and export-ready outputs so you can monitor market-moving updates in real time.

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.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

Use Composio’s Airtable toolkit through Rube MCP to automate creating, querying, and managing records, fields, schemas, and comments while following the required tool discovery, pagination, and limit-handling steps.

A comprehensive guide on integrating Algolia search into modern React applications. This skill covers utilizing React InstantSearch with custom hooks (like useSearchBox, useHits) for type-ahead search, handling Next.js Server-Side Rendering (SSR) with InstantSearchNext, and implementing best practices for data synchronization and indexing strategies (e.g., partial updates, batching). Learn how to build high-performance, scalable search features.