Skill UI

TileDB-VCF lets you ingest, store, query, and export large genomic VCF/BCF datasets using TileDB’s sparse arrays, supporting incremental sample growth, parallel regions queries, and cloud-local interoperability for population genomics workflows.

A comprehensive toolkit designed to track and analyze content performance, engagement metrics, and Return on Investment (ROI) across major social media platforms. By utilizing specialized Apify Actors, this skill extracts deep insights from Instagram, Facebook, YouTube, TikTok, and more, helping users identify high-performing content, monitor growth, and measure marketing campaign effectiveness.

This guide teaches advanced methodology for identifying target species using multiple biological features. Users learn to combine distinct clues such as habitat, morphology, behavior, and geographic range. A key technique involves first identifying the country through demographic clues (e.g., population growth, endemic status) to significantly narrow the search scope before searching for the specific species.

COBRApy is a powerful Python library for Constraint-Based Reconstruction and Analysis (COBRA). It enables deep dives into cellular metabolism, allowing users to build, simulate, and analyze complex metabolic models (e.g., genome-scale models). Core functionalities include Flux Balance Analysis (FBA), Flux Variability Analysis (FVA), performing gene/reaction knockouts, and optimizing growth media. It supports standard metabolic model formats like SBML, JSON, and YAML.

This guide provides a comprehensive method for setting up Snowflake observability. It teaches how to leverage built-in ACCOUNT_USAGE views to monitor critical metrics such as query performance (latency, error rates), credit consumption, and storage growth. Furthermore, it demonstrates how to implement automated alerts for operational issues (e.g., high failure rates, stuck warehouses) and export metrics to external monitoring systems like Grafana or Prometheus.

A structured framework for performing in-depth cohort analysis. It helps measure user retention rates, estimate Lifetime Value (LTV), and segment users based on behavior over time. Use this when analyzing growth metrics, identifying churn risks, or presenting product health to leadership.