Skill UI

Automates dbt test creation within data pipelines, offering guided best practices, code generation, and validation to keep ETL, transformation, and streaming workflows reliable.

This sophisticated workflow guides users from a broad research concept or initial topic to a fully validated, actionable, and pilot-tested research idea. It orchestrates a multi-stage pipeline, including literature surveys, novelty checks, detailed idea generation, critical review, and the creation of a refined experimental plan, ensuring the output is a robust proposal ready for implementation.

Hypogenic is an advanced framework that uses Large Language Models (LLMs) to systematically generate and test hypotheses on complex tabular datasets. It supports three powerful modes: data-driven generation (HypoGeniC), literature-grounded refinement (HypoRefine), and combined union methods. This tool is essential for accelerating scientific discovery in fields such as predictive modeling, content analysis, and deception detection by combining empirical data insights with theoretical knowledge.

A systematic tool for scientific inquiry, guiding users through the entire research process. It helps formulate testable, mechanistic hypotheses from raw observations or preliminary data, following the established scientific method. The workflow includes comprehensive literature review, evidence synthesis, generating competing explanations, and designing experiments, all while mandating the creation of scientific schematics for visualization.

A comprehensive guide and toolkit for systematic statistical analysis, covering the entire research lifecycle. Features include test selection guidance (t-test, ANOVA, regression), rigorous assumption checking (normality, homogeneity), power analysis, effect size calculation, and generation of professional, APA-formatted academic reports. Essential for academic researchers and data scientists.