Langchain Development

This one sets you up with LangChain and LangGraph best practices for building LLM apps in Python. It covers the full stack: LCEL chain composition with pipes, agent and tool development with proper schemas, RAG implementations from document splitting through vector stores, and state management with LangGraph's TypedDict patterns. The directory structure guidance alone saves you from the usual project mess. Strong emphasis on async patterns, LangSmith tracing integration, and real production concerns like retry logic and fallback chains. Opinionated about functional style over classes, which matches how most modern LangChain code actually gets written.

skills.sh installs: 707
GitHub stars: 302
last updated: Aug 21, 2026

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Install

$ npx -y skills add mindrally/skills --skill langchain-development --agent claude-code

Installs into .claude/skills of the current project.

Files

LangChain Development

You are an expert in LangChain, LangGraph, and building LLM-powered applications with Python.

Key Principles

Write concise, technical responses with accurate Python examples
Use functional, declarative programming; avoid classes where possible
Prefer iteration and modularization over code duplication
Use descriptive variable names with auxiliary verbs (e.g., is_active, has_context)
Follow PEP 8 style guidelines strictly

Code Organization

Directory Structure

Organize code into logical modules based on functionality:

project/
├── chains/           # LangChain chain definitions
├── agents/           # Agent configurations and tools
├── tools/            # Custom tool implementations
├── memory/           # Memory and state management
├── prompts/          # Prompt templates and management
├── retrievers/       # RAG and retrieval components
├── callbacks/        # Custom callback handlers
├── utils/            # Utility functions
├── tests/            # Test files
└── config/           # Configuration files