Skill UI

A comprehensive, end-to-end workflow for building product features guided by rigorous data insights. This skill guides users through the entire lifecycle, encompassing exploratory data analysis, forming measurable business hypotheses, designing statistical A/B tests, architecting data pipelines, and implementing robust backend/frontend instrumentation. It ensures that every feature iteration is validated by data and measured for impact.

A comprehensive guide to setting up a minimal working example on Databricks. This skill covers creating a development cluster, uploading and executing notebooks (Python SDK), running tasks as jobs, and setting up a Serverless SQL Warehouse. It is ideal for new users or initializing a basic data engineering project.

Guides data-engineering teams through configuring incremental loads in pipelines, covering best practices, orchestration tips, example code, and output validation for streaming or batch workflows.

Guides senior data scientists through designing controlled experiments, engineering structured features, training ML models, and translating results into decisions using Python, R, SQL, and tools like Scikit-learn, XGBoost, and MLflow.

Guides multi-location retailers through demand forecasting, safety stock calculation, replenishment planning, promotional lift estimation, and ABC/XYZ segmentation, blending statistical best practices with ML-ready feature engineering to balance service levels and inventory costs.

Access the BRENDA SOAP API to retrieve enzyme kinetic parameters, reaction equations, organism-specific data, and environmental preferences, enabling biochemical research, metabolic pathway reconstruction, and enzyme engineering workflows.

Access RCSB PDB to search biomolecular structures via text, sequence, or geometry queries, download coordinate files in PDB/mmCIF/Binary formats, retrieve metadata, and batch-process entries for drug discovery, protein engineering, and structural biology pipelines.

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Pandas is a Python data analysis library that simplifies loading, cleaning, transforming, and analyzing structured datasets, making it ideal for data wrangling in analytics or engineering workflows.

This skill provides a comprehensive method for querying advanced threat intelligence using ATT&CK data. It maps the Tactics, Techniques, and Procedures (TTPs) of specific APT groups into MITRE ATT&CK Navigator layers. Users can build multi-layer heatmaps to identify detection gaps, compare threat coverage, and create structured threat-intel reports for defense engineering.

This system deploys advanced AI, NLP, and behavioral analytics to detect sophisticated Business Email Compromise (BEC) attacks. Unlike traditional filters that rely on malicious links, this solution analyzes writing style, tone, grammatical patterns, and contextual anomalies to identify pure social engineering impersonation, providing a robust defense layer against advanced phishing and fraud.

Performs constraint-based metabolic modeling with COBRApy: FBA, pFBA, FVA, gene knockouts, flux sampling, medium design, production envelopes, gap filling, and SBML model validation for systems biology and metabolic engineering.