implement-connector

Implement Python data source connectors conforming to the LakeflowConnect interface.

60|116|Updated Nov 13, 2025

Search Tags:#api #python #implementation #connector #data source #lakeflow

Ready to run

npx skills add https://github.com/databrickslabs/lakeflow-community-connectors --skill implement-connector

💬 Prompt for ChatGPT / Claude Chat▼

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

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill automates the implementation of Python connectors for various data sources, ensuring they adhere to the LakeflowConnect interface and specific data handling requirements.

Core Features & Use Cases

Connector Implementation: Generates Python code for data source connectors based on API documentation.
Interface Conformance: Ensures connectors implement the LakeflowConnect interface correctly.
Data Handling: Enforces requirements for schema definition, incremental reads, and delete handling.
Use Case: After researching a new API, use this Skill to quickly generate the boilerplate Python code for a Lakeflow connector, significantly speeding up development.

Quick Start

Implement the Python connector for the 'salesforce' source using the existing API documentation.

Frequently Asked Questions about implement-connector

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

FAQPage Schema

How do I implement a Python data source connector for Lakeflow Connect?▼

To implement a Python data source connector for Lakeflow Connect, you can use this Skill to generate compliant Python code from your API documentation, ensuring correct schema definition, incremental reads, and delete record processing.

How do I handle incremental reads and cursor management in a Python connector?▼

This Skill handles incremental reads and cursor management in a Python connector by supporting strategies like sliding time-windows and server-side limits, automatically implementing the required logic based on your API documentation.

What is the Lakeflow Connect interface and how does it structure connector implementation?▼

The Lakeflow Connect interface is a specification for Python data source connectors that this Skill enforces, structuring your implementation around schema definition, incremental reads with cursor management, and delete record processing.

Can I generate a Python connector from API documentation for any data source?▼

Yes, you can generate a Python connector from API documentation for any data source. This Skill uses your existing API documentation to quickly generate the boilerplate Python code conforming to the Lakeflow Connect interface.

Does this Python connector implementation support processing delete records?▼

Yes, this Python connector implementation supports processing delete records. It enforces specific data handling requirements to ensure delete records are correctly processed alongside schema definition and incremental reads.

What strategies are available for incremental reads when building a Python data source connector?▼

When building a Python data source connector, this Skill supports incremental read strategies including sliding time-windows and server-side limits, ensuring efficient data ingestion and cursor management.