Skill UI

A guide to production-ready patterns for dbt (data build tool). It covers essential practices for robust data pipeline development, including model organization (staging, intermediate, marts), implementing advanced testing strategies, ensuring data quality, and optimizing incremental processing for large datasets.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

Automates file format conversion guidance within data pipelines, covering ETL, workflow orchestration, data transformation, and streaming standards with production-ready examples.

This workflow provides the primary mechanism for converting complex, live websites into clean, LLM-ready markdown content. It supports single-page scraping using `scrapeUrl`, deep multi-page site crawling with `crawlUrl`, and scalable async crawling for large datasets. Ideal for building robust content ingestion pipelines and reliably extracting structured information from the web.

Automates Kafka stream processor workflows within data pipelines, offering step-by-step guidance, best practices, production-ready code and configurations, and validation to support ETL, data transformation, workflow orchestration, and streaming data processing.

This guide provides comprehensive patterns and best practices for designing high-impact Key Performance Indicator (KPI) dashboards. It covers everything from establishing a KPI framework (Strategic, Tactical, Operational) to defining departmental metrics (Sales, Marketing, Finance) and implementing best-practice layouts for executive summaries and SaaS metrics. Use this skill to translate raw data into actionable business insights.

Automates Luigi task generation for data pipelines, offering guided best practices, code and configuration outputs, and validation to streamline ETL, transformation, orchestration, and streaming workflows.

Auto-activating skill guiding metadata extractor implementations within data pipelines, covering ETL, transformation, orchestration, and streaming best practices.

Track experiments, catalog models, compare versions, and deploy trained artifacts with MLflow’s framework-agnostic lifecycle tools across PyTorch, TensorFlow, and other stacks.

Performs OpenEvidence DeepConsult queries to synthesize comprehensive medical literature, score confidence, and format structured clinical summaries for complex research and decision support.

Pandas Pro orchestrates DataFrame transformations for cleaning, aggregating, merging, resampling, and optimizing large tables using vectorized patterns and validation checks, ideal for analytics pipelines.

Enables regression modeling via the regression-analysis-tool plugin by analyzing datasets, choosing linear or polynomial fits, validating models, and reporting metrics so users can predict outcomes or explore variable relationships.