Skill UI

Gtars is a robust toolkit providing native Rust implementations and Python bindings for advanced genomic interval modeling. It supports core bioinformatic tasks including set algebra, coverage calculation, tokenization, and fragment processing. The library emphasizes data integrity by enforcing strict genomic data contracts, ensuring correct handling of coordinates, assemblies, and reference sequences across complex computational pipelines.

This comprehensive guide provides advanced workflows for interacting with ClickHouse. It covers efficient bulk data insertion (CSV, JSON), complex analytical querying (funnel analysis, retention), implementing materialized views for pre-aggregation, and utilizing powerful window functions. Ideal for building high-performance OLAP dashboards and executing large-scale data transformations.

A comprehensive guide to designing high-performance schemas in ClickHouse. Learn how to select the optimal MergeTree engine (e.g., ReplacingMergeTree, AggregatingMergeTree), structure the ORDER BY key for maximum query efficiency, and implement effective partitioning strategies for large-scale analytical workloads and ETL processes.

This workflow utilizes the SerpApi to perform advanced, multi-engine searches across platforms including YouTube, Bing, Google News, Google Shopping, and Google Maps. It is essential for data scraping, SEO research, and building comprehensive search features that require aggregating results from diverse sources beyond standard Google search. Use it to collect videos metadata, product pricing, local business details, and news articles efficiently.

A comprehensive guide for executing core Snowflake data loading workflows. This covers setting up stages (S3, GCS, Azure), defining file formats (CSV, JSON, Parquet), and using COPY INTO for bulk loading. It also details advanced continuous ingestion using Snowpipe, enabling real-time data synchronization from cloud storage into Snowflake tables.

Master the creation of robust ELT (Extract, Load, Transform) data pipelines within Snowflake. This guide demonstrates advanced data orchestration using Snowflake Streams for Change Data Capture (CDC), Tasks for scheduled transformations and DAG management, and Dynamic Tables for declarative, auto-refreshing data models. Ideal for building sophisticated, scalable data warehousing solutions.

This skill simulates a Chief Data Officer's deep interrogation, pressure-testing any plan touching core data strategy. Use it before approving ML model training runs, selecting data infrastructure (warehouse vs. mesh), productizing data assets, or making major team hires. It ensures that all data decisions are grounded in clear business decisions, compliance, and structural necessity.

This guide provides an expert framework for implementing robust analytics tracking, ensuring all data collected is actionable. It covers core principles, event naming conventions, defining key user actions (e.g., signups, purchases), and practical setup using industry tools like GA4 and Google Tag Manager (GTM). Use this when auditing existing tracking, defining conversion funnels, or measuring marketing campaign performance.

An interactive workflow for auditing and standardizing naming conventions (tables, columns, measures) in Power BI semantic models (TMDL). It ensures names are human-readable, consistent, and aligned with core business terminology, addressing technical issues like abbreviations, mixed casing, and inappropriate technical prefixes.

This skill processes raw, unstructured materials (e.g., conversations, reports, controversies) into highly structured, reusable casebook entries. It focuses on identifying core facts, defining mechanism chains, and mapping responsibility links while ensuring rigorous data anonymization. It is designed for deep knowledge management and pattern recognition, helping users build a reliable, reusable knowledge base for future professional analysis, rather than generating immediate advice.

This skill provides querying capabilities for the 1000 Genomes Project dataset, covering 3,202 whole-genome-sequenced individuals on the GRCh38 assembly. It allows users to identify variants within a region, find specific individuals who carry defined genetic variants, or determine the relatedness between two named individuals. Results include allele frequencies (AF), AlphaMissense scores, and various clinical annotations. Use this tool for advanced genomic cohort analysis.

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.