py-sas-studio-custom-steps 2.0.0

A package containing a Python class and associated methods, along with an MCP server script to create and manipulate SAS Studio Custom Step files.

py-sas-studio-custom-steps: A Python package for SAS Studio Custom Steps

This repository provides a Python class and associated methods to create, access and manipulate SAS Studio Custom Steps. SAS Studio Custom Steps are low-code components designed for analytical and data engineering operations on the SAS Viya platform. They provide a user interface to accept parameters which are then executed by an associated SAS program. Custom Steps provide easy access to common operations, boost code reusability, and help establish best practices in programming.

As SAS Viya continues to welcome a diverse set of users with different preferences (e.g. programming, low-code / no-code), a Python-based framework for interacting with custom steps accelerates code reusability and assists rapid development and testing of custom steps. This package can be used to access and port custom step capabilities from SAS Studio and non-SAS Studio applications, such as SAS Viya Workbench, Visual Studio Code, Claude Code and other Python IDEs.

Note that SAS Viya, SAS Viya Workbench etc. refer to commercial software provided by SAS Institute. The open-source package in this repository is not officially provided or supported by SAS Institute. It only contains a convenient tool for manipulating a SAS Studio Custom Step, a serialized file. It does not execute SAS algorithms or procedures. SAS programs and SAS Studio Custom Steps require a SAS / SAS Viya license to execute.

A wiki of this repo has been generated using DeepWiki and is available here:

📢 New Announcement - NOW AVAILABLE AS AN MCP SERVER SCRIPT!!!

Important update: Methods forming part of the Python class used in this package, CustomStep, are now also accessible as part of an MCP server. This MCP server script is located here: stdio_mcp_script.py and can be served over stdio.

This means that you can now interact with py-sas-studio-custom-steps and build your own custom step through AI agents!!!

Installation

1. Clone this repository
2. To install locally in editable mode, refer here

Run the following command for a pip installation of the package from PyPi.

pip install --upgrade py-sas-studio-custom-steps

Usage - quick example

To import the package inside a Python script:

import py_sas_studio_custom_steps as py_sas_step

custom_step = py_sas_step.CustomStep()

or, as an alternative, import the object directly:

from py_sas_studio_custom_steps import CustomStep

custom_step = CustomStep()

Refer this notebook for a detailed example.

Key Operations

This package will evolve and add more functionality in a gradual manner. Important operations that are currently available include:

• extract_sas_program: Extract the SAS program from a given custom step
• create_custom_step: Write a custom step (CustomStep) object to a SAS Studio Custom Step (.step) file
• add_starter_page and add_about_page: Rapidly create a starter SAS Studio Custom Step and edit further
• attach_sas_program: Attaches content of a SAS program to a custom step object.
• attach_ui: Attaches UI component definitions to a custom step object.

and, as a bonus, some convenient generative AI functionality (note that this also requires a Gemini API Key from Google which is used to call a Gemini Flash 3.5 Large Language Model (LLM))

• generate_readme: Creates a README file as per user specification.
• create_sas_program: Creates a SAS program through an LLM's assistance based on a user prompt
• modify_sas_program: Modifies the SAS program component of a custom step through an LLM's assistance based on a user prompt
• create_ui: Creates a user interface through an LLM's assistance based on a user prompt

Documentation

Refer this page for a list of all available methods and attributes.

Generative AI usage

Some functions (described in Documentation) make use of a Large Language Model (LLM)(Gemini 3.5 Flash). While you are free to modify the code to accommodate other LLMs, this is at present the only LLM supported. Read this important note regarding functions that make use of Generative AI.

IMPORTANT: All outputs returned from Generative AI tools such as LLMs should be carefully reviewed prior to actual use. Quality of Generative AI outputs are determined by the Large Language Model in use and may be incorrect. Always review the same.

Convenience: tasks.json

This repository contains a tasks.json meant for use in Visual Studio Code which helps clean up temporary files and stands up a virtual environment for quick development and exploration. Remove this file if you do not want to have Visual Studio Code run the tasks in tasks.json.

Change Log

• Version: 2.0.0 (16JUN2026)
  • Now also available as an MCP server script

Refer CHANGELOG.md for other changes.

Contact

• Sundaresh Sankaran