langchain-core-workflow-a

Featured

Build LangChain LCEL chains with prompts, parsers, and composition. Use when creating prompt templates, building RunnableSequence pipelines, parallel/branching chains, or multi-step processing workflows. Trigger: "langchain chains", "langchain prompts", "LCEL workflow", "langchain pipeline", "prompt template", "RunnableSequence".

AI & Automation 2,266 stars 315 forks Updated today MIT

Install

View on GitHub

Quality Score: 99/100

Stars 20%
100
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
50
License 10%
100
Description 5%
100

Skill Content

# LangChain Core Workflow A: Chains & Prompts ## Overview Build production chains using LCEL (LangChain Expression Language). Covers prompt templates, output parsers, RunnableSequence, RunnableParallel, RunnableBranch, RunnablePassthrough, and chain composition patterns. ## Prerequisites - `langchain-install-auth` completed - `@langchain/core` and at least one provider installed ## Prompt Templates ### ChatPromptTemplate ```typescript import { ChatPromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts"; // Simple template const simple = ChatPromptTemplate.fromTemplate( "Translate '{text}' to {language}" ); // Multi-message template with chat history slot const chat = ChatPromptTemplate.fromMessages([ ["system", "You are a {role}. Respond in {style} style."], new MessagesPlaceholder("history"), // dynamic message injection ["human", "{input}"], ]); // Inspect required variables console.log(chat.inputVariables); // ["role", "style", "history", "input"] ``` ### Partial Templates ```typescript // Pre-fill some variables, leave others for later const partial = await chat.partial({ role: "senior engineer", style: "concise", }); // Now only needs: history, input const result = await partial.invoke({ history: [], input: "Explain LCEL", }); ``` ## Output Parsers ```typescript import { StringOutputParser } from "@langchain/core/output_parsers"; import { JsonOutputParser } from "@langchain/core/output_parsers"; import { StructuredOutputParser...

Details

Author
jeremylongshore
Repository
jeremylongshore/claude-code-plugins-plus-skills
Created
7 months ago
Last Updated
today
Language
Python
License
MIT

Integrates with

Similar Skills

Semantically similar based on skill content — not just same category

AI & Automation Solid

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".

359 Updated today
majiayu000
AI & Automation Listed

langchain

Build LLM applications with LangChain and LangGraph. Use when creating RAG pipelines, agent workflows, chains, or complex LLM orchestration. Triggers on LangChain, LangGraph, LCEL, RAG, retrieval, agent chain.

2 Updated today
Makiya1202
AI & Automation Featured

langchain-hello-world

Create a minimal working LangChain example with LCEL chains. Use when starting a new LangChain integration, testing your setup, or learning LCEL pipe syntax with prompts and output parsers. Trigger: "langchain hello world", "langchain example", "langchain quick start", "simple langchain code", "first langchain app".

2,266 Updated today
jeremylongshore
AI & Automation Solid

langchain-chains

LangChain chain composition including SequentialChain, RouterChain, and LCEL patterns

1,034 Updated today
a5c-ai
AI & Automation Listed

precision-prompt-chaining

Multi-step prompt chaining skill where each step's output becomes the next step's input, with explicit rules, constraints, and reference tokens between steps. Activates when the user needs to build a complex pipeline of dependent Claude calls — research → outline → draft → edit → publish — where downstream steps must be constrained by upstream outputs without hallucination or drift. Designs chains with named output variables, explicit pass rules, validation gates between steps, and failure recovery conditions. Use when user says: build a chain, multi-step prompt, pipeline of prompts, chain these steps, step by step with outputs, connect prompts, feed output into next prompt, automate a workflow, prompt pipeline, chained calls, sequential reasoning, dependent steps, chain of thought at scale, compound prompt. Do NOT activate for: single-turn questions, simple back-and-forth conversation, cases where one prompt is sufficient for the task. First response: "Prompt Chain Builder active. Describe the full workflow

2 Updated 4 days ago
Sandeeprdy1729