Skill UI

Master the LangChain framework, the industry standard for building sophisticated and production-grade Large Language Model (LLM) applications. This skill covers core concepts including autonomous agents, multi-step chains, memory management, Retrieval-Augmented Generation (RAG), and external tool integration, enabling the development of complex, stateful AI systems.

A comprehensive guide and tool for building and configuring complex, multi-step AI agent workflows. Users can define sophisticated automation pipelines by chaining together customizable triggers (e.g., email, webhook), actions (e.g., Slack post, database update), conditional logic, and external knowledge bases. Ideal for automating business processes like customer support triage, data processing, and complex notifications, ensuring deterministic and scalable AI operations.

This skill provides comprehensive best practices for managing data within AI agent workflows. It covers data classification (PII, PHI), prompt-level controls, secure memory usage, and isolation techniques. It ensures that agents adhere to global regulations like GDPR, CCPA, and HIPAA when handling sensitive or regulated data.

This skill provides comprehensive reference architectures for integrating Lindy AI agents into production applications. It covers various complex patterns, including simple webhook callbacks, sophisticated event-driven pipelines, multi-agent delegation systems, scheduled workflows, and RAG-powered customer chat interfaces. Essential for designing and building robust, scalable, and reliable AI-powered systems.

Comprehensive patterns and best practices for deploying and managing microservices using the Linkerd service mesh on Kubernetes. Covers automatic mTLS, sophisticated traffic splitting for canary deployments, setting Service Profiles for enhanced resilience (retries/timeouts), and defining robust authorization policies.

This comprehensive guide provides production-ready patterns for building advanced LLM applications. It details the entire RAG pipeline, covering document ingestion, various chunking strategies, and advanced retrieval methods (Hybrid, Multi-query). Furthermore, it covers sophisticated agent architectures, such as ReAct, enabling complex reasoning and tool use. Ideal for developers designing enterprise-grade AI systems.

An expert guide and framework for building production-grade, sophisticated AI agent systems. This skill specializes in utilizing the latest LangChain 0.1+ and LangGraph APIs to implement multi-agent orchestration, advanced Retrieval-Augmented Generation (RAG) pipelines (e.g., RAG Fusion, HyDE), and complex state management. It covers comprehensive topics from architectural design (Plan-and-Execute, ReAct Agents) to production deployment (FastAPI streaming, LangSmith observability, caching, and robust error handling).

Develop robust, multi-channel enterprise agents designed for the Microsoft 365 ecosystem, including Teams and Copilot Studio. This SDK provides a comprehensive Python framework featuring aiohttp hosting, advanced AgentApplication routing, real-time streaming responses, and secure authentication via MSAL. Ideal for building sophisticated, production-grade bots and automation tools that integrate deeply with enterprise Microsoft services.

This comprehensive SDK provides tools for building enterprise-grade AI agents integrated across the Microsoft 365 ecosystem, including Teams and Copilot Studio. It supports advanced features such as Express hosting, structured routing, streaming responses via Azure OpenAI, and direct client integration, enabling developers to build sophisticated business automation solutions.

This skill provides methodologies for designing sophisticated, layered memory architectures for AI agents. It moves beyond simple context windows and basic vector stores by incorporating short-term, long-term, and graph-based memory systems. Implementing this is crucial for agents that require persistence across sessions, maintaining complex entity consistency, and performing advanced, relationship-driven reasoning over accumulated knowledge bases, including time-aware fact tracking.

This skill provides a comprehensive framework for monitoring Mistral AI API usage in production. It includes an instrumented client wrapper (TypeScript) to automatically capture critical metrics like latency, token consumption, cost, and error rates. Furthermore, it integrates with Prometheus for metric storage and Grafana for visualization, allowing users to set up sophisticated alerting rules for performance degradation, rate limiting, or cost spikes.

Guides researchers through drafting publication-ready machine learning papers for NeurIPS, ICML, ICLR, ACL, AAAI, and COLM by combining expert writing philosophy with LaTeX templates, citation verification APIs, and collaborative workflows.