langchain-core-workflow-a
Build LangChain chains and prompts for structured LLM workflows. Use when creating prompt templates, building LCEL chains, or implementing sequential processing pipelines. Trigger with phrases like "langchain chains", "langchain prompts", "LCEL workflow", "langchain pipeline", "prompt template".
npx skills add majiayu000/claude-skill-registry --skill langchain-core-workflow-a-byteworthy-upstream --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LangChain Core Workflow A: Chains & Prompts ## Overview Build production-ready chains using LangChain Expression Language (LCEL) with prompt templates, output parsers, and composition patterns. ## Prerequisites - Completed `langchain-install-auth` setup - Understanding of prompt engineering basics - Familiarity with Python type hints ## Instructions ### Step 1: Create Prompt Templates ```python from langchain_core.prompts import ( ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate, MessagesPlaceholder ) # Simple template simple_prompt = ChatPromptTemplate.from_template( "Translate '{text}' to {language}" ) # Chat-style template chat_prompt = ChatPromptTemplate.from_messages([ SystemMessagePromptTemplate.from_template( "You are a {role}. Respond in {style} style." ), MessagesPlaceholder(variable_name="history", optional=True), HumanMessagePromptTemplate.from_template("{input}") ]) ``` ### Step 2: Build LCEL Chains ```python from langchain_openai import ChatOpenAI from langchain_core.output_parsers import StrOutputParser, JsonOutputParser llm = ChatOpenAI(model="gpt-4o-mini") # Basic chain: prompt -> llm -> parser basic_chain = simple_prompt | llm | StrOutp
- Overview
- Prerequisites
- Instructions
- Step 1: Create Prompt Templates
- Step 2: Build LCEL Chains
- Step 3: Chain Composition
- Step 4: Branching Logic
- Output
- Examples
- Multi-Step Processing Chain
- With Context Injection
- Error Handling
- Resources
- Next Steps
What does the langchain-core-workflow-a skill do?
Build LangChain chains and prompts for structured LLM workflows. Use when creating prompt templates, building LCEL chains, or implementing sequential processing pipelines. Trigger with phrases like "langchain chains", "langchain prompts", "LCEL workflow", "langchain pipeline", "prompt template".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill langchain-core-workflow-a-byteworthy-upstream --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.