Can I run multiple LLM tasks simultaneously?

Yes, this skill provides patterns for using RunnableParallel, allowing you to execute multiple chains in parallel and aggregate their results.

Does this skill handle chat history?

Yes, it includes instructions for creating chat-style templates using MessagesPlaceholder to manage conversation history effectively.

How does the skill handle different output formats?

The skill demonstrates how to use StrOutputParser and JsonOutputParser to ensure the LLM's response is converted into a structured format usable by your application.

Can I add custom logic between chain steps?

Absolutely. The skill covers chain composition where you can inject custom functions or use RunnablePassthrough to manipulate data between steps.

What is LCEL in the context of this skill?

LCEL (LangChain Expression Language) is a declarative way to compose chains, making it easier to build complex workflows from smaller components like prompts and LLMs.

LangChain Chains & Prompts

Name: LangChain Chains & Prompts
Author: jeremylongshore

byjeremylongshore

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1,031

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Data Science & ML

Architects production-ready LangChain workflows using LCEL, dynamic prompt templates, and complex composition patterns.

This skill empowers developers to build structured, type-safe LLM applications by leveraging LangChain Expression Language (LCEL). It provides standard implementation patterns for creating chat-style prompt templates, composing complex chains (sequential, parallel, and branching), and implementing robust output parsing. It is specifically designed to help transition from basic LLM prompts to production-grade AI pipelines where consistent data flow and logical control are paramount.

Key Features

01Integrate conditional branching logic for intelligent LLM routing

02Standardize data flow with built-in output parsers and context injection

03Build declarative workflows using LangChain Expression Language (LCEL)

04Implement parallel and sequential chain execution patterns

051,031 GitHub stars

06Construct reusable ChatPromptTemplates with advanced variable substitution

Use Cases

01Developing multi-step AI reasoning and data processing pipelines

02Creating complex chat interfaces with managed history and dynamic context injection

03Building automated content generation tools with specific formatting requirements

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills langchain-core-workflow-a

For use in Claude.ai and ChatGPT

Download Skill

Key Features

01Integrate conditional branching logic for intelligent LLM routing

02Standardize data flow with built-in output parsers and context injection

03Build declarative workflows using LangChain Expression Language (LCEL)

04Implement parallel and sequential chain execution patterns

051,031 GitHub stars

06Construct reusable ChatPromptTemplates with advanced variable substitution

Use Cases

01Developing multi-step AI reasoning and data processing pipelines

02Creating complex chat interfaces with managed history and dynamic context injection

03Building automated content generation tools with specific formatting requirements

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills langchain-core-workflow-a

For use in Claude.ai and ChatGPT

Download Skill