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.

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 guide details how to deploy OpenCTI, a powerful, open-source threat intelligence platform, and utilize the pycti Python client. It teaches analysts how to model complex cyber threat intelligence, including threat actors, campaigns, and indicators, into a structured STIX 2.1 knowledge graph. Use this for centralized threat-intel correlation, building adversary graphs, and generating STIX bundles for detection engineering.

This skill guides the design of real-data validation for academic manuscripts, specifically for forecasting and financial engineering strands. It covers data provenance, rolling out-of-sample tests, benchmark batteries, and statistical significance of improvements. It ensures robustness against look-ahead bias and parameter sensitivity, helping authors meet rigorous external review standards for empirical studies in journals like Chinese Journal of Management Science.

A comprehensive guide for positioning database research papers for top-tier conferences like ICDE. It teaches authors how to distinguish genuine novelty (new mechanisms) from engineering delta (implementation improvements), conduct deep literature audits, and compare their work against both academic venues and real-world production systems. Essential for improving manuscript eligibility and impact.

This guide outlines comprehensive standards for scientific reproducibility in data engineering. It details best practices for pinning hardware, software, datasets, and variance protocols, ensuring that all performance claims are verifiable and re-runnable by peers, making reproducibility a scored component of academic submission.

A comprehensive guide to the rigorous, multi-stage peer review process for top-tier data engineering conferences like ICDE. It details the two-round structure, area-chair coordination, single-blind dynamics, and the high standards set by 'builder-heavy' reviewers. Use this to strategize paper submission, understand key evaluation dimensions (novelty, soundness, evaluation), and anticipate common review objections.

A comprehensive audit checklist designed for authors submitting data-engineering systems papers to major conferences like ICDE. It covers critical areas including IEEE formatting standards, round selection strategy, Microsoft CMT conflict declarations, and the requirement for supplemental material, ensuring authors mitigate common desk-reject risks.