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migrate-functions-connector

Migrate Fivetran Functions connectors to CSDK Python projects.

70|Updated Mar 21, 2026
Search Tags:#serverless#migrate#code-porting#fivetran#connector-sdk#functions-connector#sync-migration
One-click install
npx skills add https://github.com/fivetran/connector_sdk_tools --skill migrate-functions-connector
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: migrate-functions-connector
Source: https://github.com/fivetran/connector_sdk_tools/tree/main/claude-code/skills/migrate-functions-connector
Command: npx skills add https://github.com/fivetran/connector_sdk_tools --skill migrate-functions-connector

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually porting a Fivetran Functions connector (hosted on AWS Lambda, Azure Functions, Google Cloud Functions, or other serverless platforms) to a native Fivetran Connector SDK (CSDK) project is time-consuming, error-prone, and requires deep expertise in both the Functions connector framework and CSDK rules, leading to lost sync logic, broken state handling, and invalid schema definitions if done incorrectly.

Core Features & Use Cases

  • Automated Concept Mapping: Provides an explicit mapping of all Functions connector concepts (request.secrets, request.state, insert/delete/softDelete outputs, hasMore pagination) to their CSDK equivalents, eliminating guesswork during translation.
  • Guided End-to-End Workflow: Walks users through inventorying the source Functions connector, porting implementation code to Python, updating configuration and dependency files, and validating the migrated CSDK project against SDK constraints.
  • Use Case: If you have an existing AWS Lambda Fivetran Functions connector that syncs customer data with incremental state and soft-delete support, this skill will automatically convert it to a valid CSDK connector while preserving all original sync behavior and state semantics.

Quick Start

Use the migrate-functions-connector skill to port your existing Fivetran Functions connector to a Connector SDK project, following the guided workflow to preserve all sync logic, state handling, and operation mappings.

Frequently Asked Questions about migrate-functions-connector

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

FAQPage Schema
How do I migrate an AWS Lambda Fivetran Functions connector to the Connector SDK?▼

To migrate an AWS Lambda Fivetran Functions connector to the Connector SDK, use a guided workflow that inventories source logic, ports implementation code to Python, and generates a valid CSDK project with correct connector.py, configuration.json, requirements.txt, and README.md files.

What is the process for porting serverless connector logic to Python CSDK?▼

Porting serverless connector logic to Python CSDK involves mapping Functions concepts like request.secrets and request.state to their CSDK equivalents, translating implementation code to Python, and validating the project against SDK operation rules and schema constraints.

Does migrating Fivetran Functions to CSDK preserve incremental sync and soft-delete behavior?▼

Yes, migrating Fivetran Functions to CSDK preserves incremental sync and soft-delete semantics by explicitly mapping insert, delete, and softDelete outputs alongside hasMore pagination logic to native Connector SDK equivalents during the code translation process.

Can I use this migration workflow for Azure Functions or Google Cloud Functions connectors?▼

You can use this migration workflow for Azure Functions or Google Cloud Functions connectors because it applies to porting existing Fivetran Functions connectors built on any serverless platform to a native Fivetran Connector SDK Python project.

What files are generated when porting a Fivetran Functions connector to CSDK?▼

Porting a Fivetran Functions connector to CSDK generates a valid project structure containing connector.py, configuration.json, requirements.txt, and README.md files, all validated against CSDK schema constraints and testing requirements.

Why does manual code porting from serverless functions to CSDK fail?▼

Manual code porting from serverless functions to CSDK frequently fails because it requires deep expertise in both frameworks, leading to lost sync logic, broken state handling, and invalid schema definitions if translation is done incorrectly.

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