What is the Transformation Boundary Rule?

It is a core principle of this skill requiring all data transformations, joins, and aggregations to happen in dbt, while Streamlit is used strictly for data display and interactive filtering.

How does the 'single-step' workflow work?

To ensure accuracy, the skill performs exactly one action (like generating a single chart or updating one dbt model) and then yields to the user for feedback before proceeding.

Which database tools does this skill support?

It is optimized for DuckDB as a local analytical warehouse, but it is compatible with any database supported by dbt, including PostgreSQL and BigQuery.

Can I modify raw research data with this skill?

No, the skill treats source datasets as immutable to maintain scholarly integrity; any cleaning or reformatting must be handled through downstream dbt staging models.

Academic Data Analyst

Name: Academic Data Analyst
Author: nicsuzor

bynicsuzor

0•

Data Science & ML

Facilitates reproducible academic research and data pipelines using dbt and Streamlit while enforcing rigorous data integrity standards.

The Academic Data Analyst skill streamlines computational research by implementing the academicOps methodology for data-driven projects. It ensures research integrity through a strict Transformation Boundary Rule, where all data logic is handled via version-controlled dbt models and visualizations are rendered through Streamlit. By enforcing the immutability of source data and requiring automated testing for every transformation, this skill helps researchers build transparent, auditable, and reproducible pipelines suitable for scholarly publication and peer review.

Key Features

01Implements a collaborative, single-step workflow for incremental analysis and feedback.

020 GitHub stars

03Supports comprehensive statistical analysis and automated research documentation.

04Automates the creation and testing of layered dbt models (Staging, Intermediate, Marts).

05Enforces the Transformation Boundary Rule to separate logic (dbt) from display (Streamlit).

06Protects research integrity by treating source datasets as immutable, sacred records.

Use Cases

01Building reproducible data pipelines for peer-reviewed academic publications.

02Managing complex data transformations with automated quality testing in dbt.

03Creating interactive research dashboards using Streamlit and DuckDB.

Key Features

01Implements a collaborative, single-step workflow for incremental analysis and feedback.

020 GitHub stars

03Supports comprehensive statistical analysis and automated research documentation.

04Automates the creation and testing of layered dbt models (Staging, Intermediate, Marts).

05Enforces the Transformation Boundary Rule to separate logic (dbt) from display (Streamlit).

06Protects research integrity by treating source datasets as immutable, sacred records.

Use Cases

01Building reproducible data pipelines for peer-reviewed academic publications.

02Managing complex data transformations with automated quality testing in dbt.

03Creating interactive research dashboards using Streamlit and DuckDB.