aops-extras/skills/dbt/SKILL.md
dbt (data build tool) implementation of the analyst transformation layer. Use when a project has a dbt/ directory or you need to build, test, or document SQL transformations as version-controlled, reproducible dbt models. This is the dbt-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
npx skillsauth add nicsuzor/academicops dbtInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
This skill is the dbt-specific implementation of the transformation layer described
in the tech-agnostic analyst skill (aops-tools). The analyst skill owns the
principles (all transformation in a versioned/tested/reproducible layer; presentation
displays pre-computed data only); this skill owns the dbt how-to.
dbt is one swappable choice of transformation layer. The analyst principles hold regardless of which engine you use; only the commands and file layout below are dbt-specific.
dbt/ directory (dbt/models/, dbt_project.yml).tools
Streamlit implementation of the analyst presentation layer. Use when building or updating a Streamlit dashboard that displays pre-computed research data. This is the Streamlit-specific HOW for the tech-agnostic principles in the aops-tools analyst skill — display only, never transform.
tools
Python plotting and statistical-modelling libraries (matplotlib, seaborn, statsmodels) for the analyst presentation and statistical-methodology layers. Use when producing publication-quality figures or fitting statistical models in Python. Library-specific HOW for the tech-agnostic principles in the aops-tools analyst skill.
development
Core academicOps skill — institutional memory, strategic coordination, workflow routing, and framework governance. Merges butler (chief-of-staff) with framework development conventions.
development
Support academic research data analysis with technology-agnostic principles — research-data immutability, a versioned/tested/reproducible transformation layer, statistical methodology, and self-documenting research. Use this skill for any computational research project with an empirical data pipeline. The skill enforces academicOps best practices for reproducible, transparent research with a collaborative single-step workflow. Tech-specific how-to (dbt, Streamlit, Python plotting/stats) lives in the aops-extras package.