Does it support hot-reloading?

Yes, it provides instructions and configurations for using watch modes (like pytest-watch) to automatically trigger tests whenever you save your code.

Can I use this for existing LangChain projects?

Yes, while it's great for new setups, you can use it to refactor existing projects into a standardized structure and add robust testing capabilities.

What testing frameworks are supported?

This skill primarily configures pytest along with pytest-asyncio for handling asynchronous LangChain calls and pytest-cov for monitoring test coverage.

How does this skill help reduce AI development costs?

It sets up specialized mock fixtures in pytest that simulate LLM responses, allowing you to test your application logic and chain flow without making expensive calls to external APIs.

LangChain Local Dev Loop

Name: LangChain Local Dev Loop
Author: jeremylongshore

byjeremylongshore

•

982

•

安全与测试

Streamlines LangChain application development by configuring project structures, mock testing environments, and automated dev workflows.

The LangChain Local Dev Loop skill is designed to accelerate the development of AI-powered applications by establishing a robust, standardized local environment. It automates the setup of project architectures, configures pytest with specialized fixtures for mocking LLM responses to save on API costs, and integrates hot-reload capabilities for rapid iteration. Whether you are building complex chains or autonomous agents, this skill ensures your development cycle is efficient, testable, and follows LangChain best practices from the first line of code.

主要功能

01Pre-configured pytest fixtures for LLM and prompt mocking

02982 GitHub stars

03Automated LangChain project directory structure generation

04Support for both unit testing (mocked) and integration testing (real API)

05Comprehensive pyproject.toml configuration for dev dependencies

06Built-in error handling for common environment and async testing issues

使用场景

01Developing and debugging AI agents without incurring live LLM API costs

02Initializing a new LangChain project with production-grade testing standards

03Establishing a rapid 'code-test-refactor' loop for complex LangChain chains

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills langchain-local-dev-loop

For use in Claude.ai and ChatGPT

Download Skill

主要功能

01Pre-configured pytest fixtures for LLM and prompt mocking

02982 GitHub stars

03Automated LangChain project directory structure generation

04Support for both unit testing (mocked) and integration testing (real API)

05Comprehensive pyproject.toml configuration for dev dependencies

06Built-in error handling for common environment and async testing issues

使用场景

01Developing and debugging AI agents without incurring live LLM API costs

02Initializing a new LangChain project with production-grade testing standards

03Establishing a rapid 'code-test-refactor' loop for complex LangChain chains

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills langchain-local-dev-loop

For use in Claude.ai and ChatGPT

Download Skill