Skill UI

This module implements comprehensive Role-Based Access Control (RBAC) for multi-tenant LangChain applications. It provides granular permission models for controlling model access, tool execution, and chain invocation. Key features include multi-tenant data isolation in vector stores, enforcing usage quotas (token limits), and integrating middleware for robust security enforcement. Ideal for building secure, scalable, and compliant enterprise LLM systems.

This blueprint provides a scalable, production-grade reference architecture for advanced LLM applications using LangChain. It guides the implementation of layered design, provider abstraction (allowing seamless switching between LLMs like OpenAI and Anthropic), dynamic chain registry, robust RAG pipelines, and complex multi-agent orchestration. Ideal for building enterprise-level, maintainable, and highly flexible AI systems.

This guide details setting up enterprise-grade access control for Langfuse. Learn how to configure role-based permissions (Owner, Admin, Member), implement scoped API keys, integrate SSO (SAML/OIDC), and set up comprehensive audit logging. It is essential for managing multi-team isolation and ensuring compliance in production environments.

This guide provides production-grade architecture patterns for implementing robust LLM observability using Langfuse. It covers singleton SDK setup, context propagation across asynchronous boundaries, Express middleware for automatic tracing, and cross-service trace correlation using OpenTelemetry. Ideal for designing enterprise-level, scalable tracing infrastructure for LLM applications.

This comprehensive guide details how to configure robust Role-Based Access Control (RBAC) for Lindy AI workspaces. It is essential for setting up team permissions, managing complex workspace access, and enforcing enterprise security policies. Learn how to implement advanced features like SSO, SCIM synchronization, and detailed audit logging to ensure data security and separation of duties across large organizations.

This comprehensive guide outlines essential security best practices for implementing and using Lindy AI agents. It covers crucial topics such as robust API key management, webhook signature verification, strict agent permission scoping, and secure data handling protocols. Use this resource when auditing agent activity, configuring enterprise security features (SSO/SCIM), or ensuring compliance and minimizing the attack surface area of autonomous AI systems.

This comprehensive guide demonstrates how to implement robust Role-Based Access Control (RBAC) for applications deeply integrated with Linear. It covers utilizing Linear's built-in roles, mapping application roles to specific OAuth scopes, implementing powerful permission guard middleware, and integrating advanced enterprise features like SAML SSO and SCIM provisioning, ensuring secure and scalable access management for large organizations.

Automates the setup and best practices for linear issue generation within enterprise workflows, offering guidance, production-ready code, and validation for governance, compliance, and integration tasks.

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.

This skill guides users on configuring advanced access controls within Lokalise Enterprise. It details how to implement fine-grained role-based access control (RBAC), manage complex permissions (Admin, Reviewer, Translator) using API calls, enforce language-level scoping, and handle team-level user assignments. Furthermore, it provides instructions for integrating Single Sign-On (SSO) and creating contributor groups for scalable enterprise management.

This SDK provides comprehensive tools for developing enterprise-grade, multi-channel agents for the Microsoft 365 ecosystem. Utilizing ASP.NET Core hosting, it supports integration with Teams, Copilot Studio, and general M365 services. Key features include robust agent application routing, structured state management, and secure authentication using MSAL.

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.