Can I use this for non-academic data projects?

Yes. While designed for academic rigor, the principles of reproducibility, automated testing, and separation of concerns are best practices for any professional data engineering workflow.

How does the 'one action at a time' workflow work?

Claude performs a single specific task (like generating one chart or updating one model) and then yields to the user for feedback. This ensures high accuracy and prevents the AI from drifting away from research goals.

How does this skill handle raw research data?

The Analyst skill treats source datasets as immutable sacred records. It will never modify or reformat raw data, protecting against accidental data corruption or scholarly misconduct.

What is the Transformation Boundary Rule?

It is a core principle of this skill that mandates all data joining, filtering, and aggregation happen in dbt. This keeps Streamlit strictly for display, ensuring all logic is version-controlled and auditable.

Academic Data Analyst

Name: Academic Data Analyst
Author: nicsuzor

bynicsuzor

0•

データサイエンスとML

Facilitates reproducible academic research by managing data transformations in dbt and interactive visualizations in Streamlit.

The Analyst skill implements the academicOps methodology to ensure scientific rigor in computational research. It enforces a strict Transformation Boundary Rule where all data logic occurs within dbt models while Streamlit serves exclusively as a display layer, guaranteeing that every finding is auditable, testable, and reproducible. By following a collaborative, single-step workflow, it helps researchers build robust data pipelines, perform rigorous statistical analysis, and create self-documenting research artifacts without compromising data integrity or scholarly standards.

主な機能

010 GitHub stars

02Supports layered dbt architecture including Staging, Intermediate, and Marts

03Automates project context discovery from research READMEs and data docs

04Integrates automated data quality testing and statistical reporting standards

05Enforces dbt-based data transformation boundaries for academic integrity

06Facilitates iterative Streamlit dashboard development with display-only logic

ユースケース

01Creating interactive Streamlit dashboards for exploratory data analysis

02Building reproducible data pipelines for peer-reviewed research papers

03Migrating inline Python transformations to version-controlled dbt models

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