Skill UI

Polars is a lightning-fast DataFrame library designed for efficient, memory-efficient data processing in Python and Rust. Built upon Apache Arrow, it supports lazy evaluation and parallel execution, making it ideal for ETL pipelines, large-scale data transformations, and scenarios where standard Pandas performance is insufficient. It excels at handling datasets up to 100GB that fit within available RAM.

This skill transforms raw genomic identifiers (VCF, HGVS, rsID) into highly annotated, consequence-predicted records. It utilizes powerful, open-source tools like Ensembl VEP and gnomAD to predict variant consequences (e.g., missense, nonsense), map coordinates, and attach population allele frequencies. Furthermore, it links these molecular findings to clinical and phenotype context extracted by OpenMed, forming a comprehensive research record.

This skill performs the crucial ETL process of mapping OpenMed-extracted, terminology-coded clinical facts (conditions, drugs, measurements) into the OMOP Common Data Model (CDM) v5.4 tables. It is used downstream after entity extraction and source coding, transforming raw NLP findings into standardized, OMOP-conformant records for OHDSI analytics.

This skill is designed to read data records from a specified source file, performing necessary transformations and cleaning to prepare the data for downstream use. It handles the core Extract, Transform, Load (ETL) process, making it ideal for data pipelines, data validation, and preparing raw data into a structured format.

bdistill transforms AI interactions into a structured, queryable knowledge base. It extracts, structures, and quality-scores domain knowledge from both closed-source AI models (in-session) and local open-source models (via Ollama). Users can build comprehensive reference datasets, Q&A pairs, or generate structured training data for traditional machine learning, ensuring validated and exportable domain expertise.

Design scalable, reliable and cost-effective batch and streaming data pipelines. Covers ETL/ELT, Lambda, Kappa and Lakehouse patterns, Airflow/Prefect orchestration, dbt and Spark transformations, Delta Lake/Iceberg storage, data quality checks and cost optimization.

This skill guides the process of transforming complex, raw data into compelling, actionable narratives. It covers the core structure (Setup, Conflict, Resolution), narrative arcs, and the three pillars (Data, Narrative, Visuals) needed to create high-impact presentations for executives, investors, and non-technical stakeholders. It ensures insights drive clear decisions.

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.

Create, configure, and manage Hugging Face Hub datasets. Supports SQL querying, transformation, filtering, exporting, and pushing subsets or custom datasets to the Hub.

Create and verify provenance manifests for weather-data inputs and derived artifacts, capturing object identity, selections, software versions, transformations, and checksums to support traceable, replayable, or reproducible workflows.