Guides academic research data analysis by enforcing reproducible dbt workflows and Streamlit visualization standards.
The Analyst skill for academicOps transforms Claude into a specialized research assistant focused on reproducible data pipelines. It strictly separates data transformation (handled via dbt) from visualization (handled via Streamlit), ensuring academic integrity and auditability. By enforcing a 'one-step-at-a-time' workflow and safeguarding immutable research data, it helps researchers build transparent, self-documenting, and fail-fast empirical pipelines for computational research.
主要功能
01Automates project context discovery from research READMEs and data documentation
02Integrates comprehensive statistical analysis guides and reporting standards
03Protects raw research data with immutable source directory enforcement
040 GitHub stars
05Streamlines Streamlit dashboard creation as a pure, interactive display layer
06Enforces a strict dbt-first transformation boundary rule for academic reproducibility
使用场景
01Creating interactive research dashboards to visualize pre-computed metrics
02Building reproducible data transformation pipelines for academic papers
03Auditing data lineage and transformation logic for peer review
