migrate-legacy-implementation

Migrates legacy Lua source connectors and updates their TypeScript interfaces and build files.

60|116|Updated Nov 13, 2025

Search Tags:#refactoring #migration #data pipeline #connector #code modernization #lakeflow

Ready to run

npx skills add https://github.com/databrickslabs/lakeflow-community-connectors --skill migrate-legacy-implementation

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: migrate-legacy-implementation
Source: https://github.com/databrickslabs/lakeflow-community-connectors/tree/main/.claude/skills/migrate-legacy-implementation
Command: npx skills add https://github.com/databrickslabs/lakeflow-community-connectors --skill migrate-legacy-implementation

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill automates the complex process of migrating outdated data connector implementations to a modern, standardized package structure, ensuring compatibility and maintainability.

Core Features & Use Cases

Automated Code Migration: Moves connector files from legacy directories to the new src/databricks/labs/community_connector/sources/ structure.
Interface Conformance: Updates connectors to implement the LakeflowConnect interface.
Dependency Management: Adjusts import paths and ensures libraries are correctly referenced.
Build & Test Updates: Regenerates necessary build files and test configurations.
Use Case: A team has several connectors developed before the new src/ directory structure was adopted. This Skill can be used to systematically migrate each connector, reducing manual effort and potential errors.

Quick Start

Migrate the legacy implementation for the 'my_legacy_source' connector.

Frequently Asked Questions about migrate-legacy-implementation

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

FAQPage Schema

How do I migrate legacy data connectors to a new package structure?▼

Migrating legacy data connectors to a new package structure is automated by moving files to a standardized `src/` directory, updating import paths, conforming to the LakeflowConnect interface, and regenerating build files.

What does conforming to the LakeflowConnect interface involve during connector modernization?▼

Conforming to the LakeflowConnect interface during connector modernization involves refactoring legacy source implementations, adjusting dependency references, and ensuring the updated code aligns with the new package structure under `src/`.

Do I need Python 3.10+ to refactor legacy source connectors?▼

Yes, you need Python 3.10+ to refactor legacy source connectors because the automated migration scripts require this specific version to update imports and regenerate build configurations correctly.

Can I update multiple outdated data connectors at once?▼

You migrate outdated data connectors by specifying a source for each run, systematically moving files and regenerating test configurations individually to reduce manual effort and potential errors across the repository.

What is the best way to update import paths for legacy data pipelines?▼

The best way to update import paths for legacy data pipelines is to use automated migration scripts that adjust library references and conform connectors to the LakeflowConnect interface within a standardized `src/` directory.

When should I not use automated code modernization for data connectors?▼

You should avoid automated code modernization for data connectors when your legacy implementations already adhere to the new `src/` directory structure or lack the specific file movement patterns required by the migration scripts.