What is the Transformation Boundary Rule in this skill?

It is a non-negotiable rule that all data transformations, joins, and aggregations must happen in dbt. Streamlit is used strictly as a display layer for pre-computed data to ensure research auditability.

Can I use this for non-academic data projects?

While designed for academic research, its strict focus on reproducibility, version-controlled transformations, and automated testing makes it excellent for any project requiring high-integrity data pipelines.

What tools are required to use the Analyst skill?

This skill is optimized for projects using dbt (data build tool) for modeling and Streamlit for interactive visualization, typically integrated within a Python-based research environment.

How does this skill protect research integrity?

The skill treats all source datasets and experimental records as sacred and immutable. It prevents any modifications to raw data, ensuring the 'ground truth' remains untampered with throughout the analysis.

Academic Data Analyst

Name: Academic Data Analyst
Author: nicsuzor

bynicsuzor

0•

데이터 과학 및 ML

Streamlines academic research data pipelines using dbt and Streamlit while enforcing rigorous reproducibility standards.

The Analyst skill is designed for computational research projects that require high levels of transparency and auditability. It implements the academicOps methodology, which mandates a strict separation between data transformation (handled exclusively in dbt) and data visualization (handled in Streamlit). By enforcing immutable source data and a single-step collaborative workflow, this skill helps researchers build self-documenting, testable, and reproducible pipelines that meet the highest scholarly standards for data integrity and statistical reporting.

주요 기능

01Strict enforcement of the dbt-only transformation boundary rule

02Automated data quality testing and validation at every pipeline stage

03Comprehensive academic methodology and statistical reporting guides

04Single-step collaborative workflow for incremental research development

05Display-only Streamlit dashboard generation for pre-computed metrics

060 GitHub stars

사용 사례

01Building reproducible data marts from raw experimental or empirical datasets

02Automating the generation of auditable methodology documentation and data tests

03Developing interactive research dashboards for peer-reviewed publication support

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add nicsuzor/aops-dist analyst

For use in Claude.ai and ChatGPT

Download Skill