Skill UI

Master the complexities of building robust Retrieval Augmented Generation (RAG) pipelines using LangChain. This guide addresses common pitfalls in vector search, such as score semantics differences (L2 vs. Cosine), embedding dimension mismatches, and chunking issues for code/Markdown. Learn best practices for selecting between FAISS, Pinecone, Chroma, and PGVector, normalizing scores, and implementing hybrid keyword+vector search for maximum retrieval quality.

This guide teaches how to build robust, production-grade ReAct agents using LangGraph 1.0's `create_react_agent`. It specifically addresses common agent failure modes, such as preventing infinite loops (recursion caps) and ensuring proper error propagation. Learn to define typed tools using Pydantic, manage state with checkpointers, and set precise recursion limits. Perfect for users migrating from legacy AgentExecutor or building reliable, multi-step tool-calling workflows.

This skill provides a comprehensive guide to building robust LangGraph 1.0 StateGraphs in Python. It covers defining typed TypedDict states with reducers, writing deterministic nodes, and defensively wiring conditional edges. It addresses common pitfalls such as silent graph termination, incorrect state updates using reducers, proper management of recursion limits, and handling evolving checkpoint schemas for reliable LLM workflow orchestration.

A comprehensive guide to ensuring robust state management for LangGraph agents. This skill addresses common checkpointing pitfalls (like missing thread_id or non-JSON serializable state) and details best practices for implementing reliable state persistence using MemorySaver or PostgresSaver. It covers thread discipline, schema migration, and time-travel debugging for production-ready applications.

This guide addresses critical LangGraph streaming pitfalls, including state overdraw, proxy buffering issues (Nginx, Cloud Run), and excessive event noise. It provides a production-grade pattern for implementing robust SSE/WebSocket endpoints using FastAPI. Learn how to correctly select the `stream_mode` (messages, updates, values) and implement server-side event filtering to reliably build live-token chat UIs and perform comprehensive state visualization.

Master advanced patterns for composing LangGraph subgraphs, which is essential for building robust, multi-agent systems like planner-executors or hierarchical teams. This skill addresses critical issues such as shared state contract propagation (P21) and callback scoping (P28). Learn to manage state across independent graph components using sophisticated dispatch patterns: compiled nodes, Send fan-out, and Command bubble-up.

This skill defines canonical, ordered middleware patterns for LangChain and LangGraph agents. It addresses critical production-level LLM deployment issues, including PII leakage across tenants, token budget overconsumption, prompt injection defense (Guardrails), and cache key corruption. Use this to build secure, scalable, and cost-aware AI applications.

A comprehensive guide to advanced, production-ready LLM inference using LangChain 1.0. This skill addresses common pitfalls and bugs when integrating multiple providers (Claude, GPT-4o, Gemini), covering issues like structured output reliability, correct streaming token accounting, and robust handling of complex AIMessage content blocks. It teaches best practices for model initialization, provider routing, and version-safe code implementation.

This guide provides methods for cost tuning and optimization for Linktree services. It addresses how high API usage from link views, clicks, and analytics queries can lead to unnecessary expenses. Techniques include implementing robust caching mechanisms (e.g., 5-min TTL for profiles, 15-min polling interval for analytics), client-side event aggregation, and auditing plan tiers to ensure efficient resource utilization and budget adherence.

This guide provides comprehensive, structured SDK patterns for interacting with the Linktree REST API. It addresses critical development challenges such as managing rate limits (429), handling authentication failures (401), and ensuring data consistency through typed objects. Patterns include a thread-safe singleton client, robust error wrappers with exponential backoff, fluent request builders for pagination, and middleware for logging and authorization, making API integration reliable and scalable.

This skill provides a comprehensive workflow for managing Lucidchart documents and diagrams using the REST API. Users can programmatically execute the full lifecycle: creating new documents, adding complex shapes and connectors via the Standard Import format, modifying existing content, and exporting the final diagram into high-resolution formats like PNG, PDF, or SVG. Ideal for automated diagram generation and content lifecycle management.

This skill provides comprehensive guidance on correctly configuring Model Context Protocol (MCP) servers within Claude Code. It details the critical concepts of configuration scope (user vs. project) and outlines best practices to prevent context pollution across different coding sessions. Users learn how to safely add, remove, and clean up MCP settings using specific JSON files, ensuring a stable and efficient development environment.