Skill UI

An autonomous research agent that plans complex investigations using Directed Acyclic Graphs (DAGs). It executes sub-questions in parallel, performs gap analysis, and iteratively refines findings before synthesizing a comprehensive, structured report. Ideal for deep market analysis, academic literature reviews, or complex problem-solving requiring multiple data sources.

A comprehensive skill for building, operating, and automating complex, reproducible genomics workloads on the DNAnexus platform. It provides deep integration with the `dx` CLI, `dxpy` Python SDK, and workflow languages like Nextflow and WDL. Use it for data transfer, job monitoring, project automation, and ensuring secure, efficient execution of large-scale biological computations.

gget is a comprehensive command-line tool and Python package offering unified access to over 20 genomic databases. It facilitates quick lookups for gene information, sequence analysis (BLAST), protein structures, and expression data, supporting both interactive CLI use and advanced programmatic Python workflows for biological research.

This skill provides a comprehensive framework for building, registering, debugging, and operating complex bioinformatics workflows on the Latch platform. It supports multiple industry-standard pipelines (Nextflow, Snakemake) and utilizes the Python SDK, CLI, and Latch Data/Registry. Use this when developing, deploying, or managing robust, data-intensive computational pipelines in a controlled environment.

PyHealth is a comprehensive Python toolkit designed for building deep-learning pipelines in the clinical and healthcare domains. It supports diverse data modalities, including EHR records, physiological signals, and medical images. The framework follows a stable 5-stage pipeline (Dataset → Task → Model → Trainer → Metrics), enabling users to perform complex tasks like predicting mortality, optimizing drug recommendations, staging sleep, or cross-mapping medical codes (ICD/ATC/RxNorm) on datasets like MIMIC-III/IV and OMOP.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables the creation of scalable, structure-preserving embeddings (2D/3D) for visualization, clustering preprocessing, and advanced machine learning feature engineering. It supports various metrics (e.g., Euclidean, Cosine) and supervised learning workflows.

A comprehensive guide to reducing expenses on ClickHouse Cloud. Learn advanced techniques for storage optimization (compression, TTL), compute resource tuning (scaling, thread limits), and query efficiency improvements. Use this when managing data budgets or analyzing high ClickHouse billing charges.

This tool collects comprehensive diagnostic data from ClickHouse system tables. It gathers crucial metrics, query logs, merge statuses, and disk usage information. It is essential for troubleshooting performance bottlenecks, preparing detailed support tickets, or investigating complex database infrastructure issues.

This guide details how to configure and manage resource constraints in ClickHouse, addressing server-side limits, user quotas, and connection management. Use it when encountering "too many simultaneous queries," optimizing resource usage, or implementing robust rate limiting strategies in applications connecting to ClickHouse. Covers SQL quota setup and client-side pooling techniques.

A comprehensive guide and implementation template for building robust data ingestion pipelines into ClickHouse. Supports multiple sources including HTTP webhooks (with batching), Kafka streams, S3, and direct SQL inserts. Demonstrates best practices for data handling, such as deduplication using ReplacingMergeTree and monitoring query logs, ensuring reliable, scalable data analytics.

This guide provides a quick reference for diagnosing and fixing the top common errors encountered in ClickHouse, including syntax errors, memory limit issues, write permission failures, and data type mismatches. It offers specific error codes, root causes, and actionable SQL solutions for smooth database operation and complex analytics.

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.