code-splitter-adapter

Standardize code splitting across programming languages using LangChain and LlamaIndex.

Updated Jun 2, 2026

Search Tags:#software development #code splitting #LangChain #programming languages #LlamaIndex

Ready to run

npx skills add https://github.com/bettercallfan/deerflow --skill code-splitter-adapter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: code-splitter-adapter
Source: https://github.com/bettercallfan/deerflow/tree/main/skills/custom/program_snippet/code-splitter-adapter
Command: npx skills add https://github.com/bettercallfan/deerflow --skill code-splitter-adapter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires langchain-text-splitters, llama-index, tree-sitter, and includes scripts (resource) components.

What problem does it solve?

This Skill provides a standardized approach to code splitting, facilitating the integration of multiple programming languages across diverse projects.

Core Features & Use Cases

Unified Interface: Offers a consistent split_code method, abstracting the underlying differences between LangChain and LlamaIndex implementations.
Multiple Strategies: Supports both LangChain and LlamaIndex code splitting strategies for flexible application.
Multi-Language Support: Handles code splitting for a variety of programming languages.
Error Handling: Provides friendly error messages for LlamaIndex parsing failures.
Metadata Support: Allows for additional metadata to be appended to split code fragments.
Use Case: For developers looking to modularize their codebase across different languages, this Skill can help streamline the process of splitting and managing code snippets.

Quick Start

To split Python code using the code-splitter-adapter, simply call the split_code method with the appropriate parameters.

Frequently Asked Questions about code-splitter-adapter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I standardize code splitting across multiple programming languages?▼

Standardizing code splitting across multiple programming languages requires a unified interface that abstracts underlying differences between LangChain and LlamaIndex. This adapter provides a consistent split_code method, streamlining the modularization of diverse codebases.

Can I use LangChain and LlamaIndex together for code parsing?▼

Yes, you can use LangChain and LlamaIndex together for code parsing. This approach supports both code splitting strategies simultaneously, offering a unified interface that abstracts their implementation differences and provides friendly error handling for parsing failures.

How do I split Python code using LangChain and LlamaIndex?▼

You split Python code by calling the split_code method with the appropriate parameters. This requires Python with langchain-text-splitters and llama-index libraries to parse and generate modular code fragments.

What is the best way to handle LlamaIndex code parsing failures?▼

Handling LlamaIndex code parsing failures is best achieved through an adapter providing friendly error messages. This ensures parsing issues during code splitting are caught and communicated clearly for effective snippet management.

Does tree-sitter support code splitting for multiple programming languages?▼

Tree-sitter supports multi-language code splitting by integrating with langchain-text-splitters and llama-index. This combination allows developers to parse and modularize code across diverse programming environments using a standardized interface.