Skill UI

This skill provides comprehensive management for team access to AI APIs, ensuring security and accountability. It allows setting up role-based access control (admin, editor, viewer), implementing per-member and total team usage quotas, and managing separate API keys across Development, Staging, and Production environments. Ideal for scaling AI usage in enterprise settings.

Automates KPI dashboard template tasks within enterprise workflows by offering guidance, best-practice patterns, production-ready code/configuration, and validation for compliance, governance, and integration scenarios.

Automates Kubernetes ConfigMap handler tasks by providing step-by-step guidance, best practice patterns, and production-ready code as part of an advanced DevOps workflow focused on Terraform, Helm, and infrastructure-as-code.

This specialized, end-to-end workflow guides the deployment of applications onto Kubernetes clusters. It systematically covers all critical production phases, including Docker container preparation, generating K8s manifests, structuring applications with Helm charts, configuring advanced service meshes (like Istio), enforcing robust security policies (RBAC, NetworkPolicy), and implementing full observability and GitOps pipelines for reliable, scalable deployments.

Auto-activating DevOps skill that guides Kubernetes deployment creation with best practices, production-ready IaC configurations, and validation across Terraform, Helm, and monitoring workflows.

Automated guidance for Kubernetes ingress configuration within advanced DevOps workflows, covering best practices, production-ready setups, and validation for infrastructure-as-code delivery.

Provides automated guidance for Kubernetes RBAC analyzer tasks within Security Advanced workflows, covering best practices, production-ready configurations, compliance checks, and threat modeling steps whenever the analyzer is mentioned.

Automates AWS Lambda layer setup by providing step-by-step guidance, validating against best practices, and generating production-ready code and deployment configurations for compute, storage, and networking scenarios.

LangChain accelerates building LLM-powered agents, RAG pipelines, and chatbots by orchestrating models, chains, tools, memory, and vector stores across major providers for rapid prototyping or production deployment.

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.

Master the art of building robust, production-grade LLM chains using LangChain Expression Language (LCEL). This guide covers advanced composition patterns, including sequential, parallel, and conditional branching chains. It also details prompt templating, structured output parsing (JSON/Zod), and context injection, enabling the creation of complex, multi-step AI pipelines.

This skill provides a comprehensive guide for deploying LangChain chains and agents into production-grade APIs. It supports multiple development stacks, including Python (LangServe/FastAPI) and Node.js (Express). Topics covered include containerization using Docker, setting up robust health checks, and deploying to major cloud platforms like Google Cloud Run and AWS Lambda. Learn how to build scalable, observable, and enterprise-grade API endpoints for advanced LLM applications.