Skill UI

This guide details how to implement webhooks for Kling AI video generation tasks. Instead of inefficiently polling the API status, you can provide a callback URL when submitting a task. When the task completes (whether successful or failed), Kling AI will automatically send a POST request containing the full result to your designated endpoint. This mechanism eliminates polling overhead and is essential for building robust, event-driven video processing pipelines.

Provides deep expertise in modern Kotlin patterns, covering structured concurrency via Coroutines and Flow, Multiplatform architecture (KMP), declarative UI using Compose, and server implementation with Ktor. Ideal for building robust, type-safe, and highly maintainable applications across various platforms.

An expert guide for designing, implementing, and optimizing large-scale, secure, and highly available cloud-native infrastructure using Kubernetes. Covers advanced topics including GitOps workflows (ArgoCD/Flux), service mesh architecture (Istio), multi-cluster management, Policy as Code (OPA), and infrastructure automation with Terraform. Ideal for platform engineers building enterprise-grade container orchestration solutions.

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.

Master the creation of autonomous AI agents using LangChain. This guide covers defining tools with schemas (Zod), utilizing `createToolCallingAgent` and `AgentExecutor` for multi-step decision-making, managing conversation memory, and implementing streaming output. Ideal for building complex, tool-augmented workflows.

This comprehensive guide details the end-to-end process of building Retrieval-Augmented Generation (RAG) pipelines using LangChain. It covers critical steps: loading diverse document types (PDF, CSV, TXT), splitting text into manageable chunks, generating high-quality embeddings (OpenAI), storing them in various vector databases (FAISS, Pinecone), and finally constructing the full runnable chain for accurate and context-aware querying.

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 comprehensive guide details advanced strategies for building highly resilient applications that interact with external LLM APIs. It covers built-in exponential backoff retries, controlling request throughput via concurrency limits, implementing provider fallbacks, and custom rate limiting using token buckets. Essential knowledge for achieving stable, high-volume API usage in production 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 comprehensive guide outlines essential, production-grade patterns for building robust LangChain applications. Learn best practices for implementing type-safe structured output using Zod, handling provider fallbacks, processing inputs concurrently with batching, optimizing user experience with streaming, and ensuring reliability through caching and retries.