Oldrich Rezek
Community@orezek
Node.js and TypeScript Developer.
Agent Skills by Oldrich Rezek
Showing 13 vetted skills indexed across 2 GitHub repositories.
orezeklanggraph-human-in-the-loop
Implements interrupt and resume patterns for human approval workflows in LangGraph.
orezeklangchain-dependencies
Manage LangChain ecosystem package dependencies and project templates.
orezekmonorepo-add-new-package
Automate adding a new package to a Node.js/TypeScript monorepo with pnpm and Turborepo.
orezeklanggraph-persistence
Configure LangGraph checkpointers, thread state, time travel, and cross-thread memory stores.
orezeklangchain-middleware
Implements human-in-the-loop approval and custom middleware for LangChain agents.
orezeklangchain-fundamentals
Create LangChain agents with create_agent, tools, middleware, and structured output.
orezekdeep-agents-orchestration
Configure subagent delegation, todo planning, and human approval interrupts in Deep Agents.
orezekdeep-agents-memory
Configure memory backends and filesystem persistence for Deep Agents.
orezeklangchain-rag
Build retrieval-augmented generation pipelines with LangChain document loaders, embeddings, and vector stores.
orezekframework-selection
Selects the appropriate LangChain, LangGraph, or Deep Agents framework layer for agent projects.
orezeklanggraph-fundamentals
Implements LangGraph agent workflows using StateGraph, nodes, edges, Command, and Send.
orezekmonorepo-add-new-app
Create deployable Node.js/TypeScript apps in a Turborepo monorepo from templates.
orezekdeep-agents-core
Create complex agents with middleware for task planning, memory, and skills.
Frequently Asked Questions About Oldrich Rezek
FAQPage SchemaWhat tasks can I accomplish using orezek's skills?▼
You can build LangChain agents with create_agent, design LangGraph state machines with nodes, edges, and checkpointing, orchestrate Deep Agents subagents with TodoList planning, add human-in-the-loop approvals, construct RAG pipelines, and scaffold pnpm/Turborepo monorepo packages and apps.
Who should use orezek's skill registry?▼
Node.js and TypeScript developers building LLM-powered agent applications benefit most. The skills target engineers choosing between LangChain, LangGraph, and Deep Agents, implementing persistence and memory backends, or managing monorepo project structure with Turborepo.
How do I get started with these LangChain and LangGraph skills?▼
Begin with the framework-selection skill before writing any agent code to determine the right framework layer. Then use langchain-dependencies to install required packages with correct minimum versions for Python or TypeScript, followed by the fundamentals skills for your chosen framework.
What prerequisites and dependencies do these skills require?▼
Skills require LangChain, LangGraph, LangSmith, or Deep Agents packages at specified minimum versions, available for both Python and TypeScript environments. RAG skills need OpenAI embeddings and a vector store such as Chroma, FAISS, or Pinecone; monorepo skills need pnpm and Turborepo.
How do the skills handle agent state persistence and memory?▼
The langgraph-persistence skill covers checkpointers, thread_id, time travel, Store, and subgraph persistence modes. The deep-agents-memory skill provides StateBackend for ephemeral storage, StoreBackend for persistent storage, FilesystemMiddleware, and CompositeBackend for routing across sessions.