Skill UI

A lightweight Python library designed for reading, parsing, and writing Flow Cytometry Standard (FCS) files. It extracts event data as NumPy arrays, handles complex metadata, and supports multiple FCS versions (2.0-3.1). Ideal for data preprocessing, building cytometry pipelines, and inspecting experimental results.

This skill proactively detects and reports comprehensive system resources, including CPU cores, GPU types (NVIDIA, AMD, Apple Silicon), available memory, and disk space. It generates a detailed JSON report with strategic, context-aware recommendations for optimizing scientific computing workflows, such as recommending parallel processing libraries (Dask, joblib), out-of-core methods (Zarr), or appropriate GPU acceleration backends (Metal, ROCm). Use this before any large-scale data analysis, model training, or computationally intensive simulation to make informed architectural decisions.

LaminDB is an open-source, lineage-native lakehouse specifically designed for complex biological datasets and models. It provides unified capabilities for querying, tracking data lineage, validating schemas, and ensuring FAIR compliance across diverse data types (e.g., AnnData, Parquet, Zarr). Use this skill when managing multi-modal genomic data, tracking computational workflows (like Nextflow/Snakemake), or building robust, reproducible data infrastructure in bioinformatics.

Polars is a lightning-fast DataFrame library built on Apache Arrow for Python. It provides an expression-based API, lazy query optimization, and parallel computation capabilities for efficient data manipulation, analytics, and ETL pipelines. It is designed to handle large, memory-intensive datasets, making it an ideal alternative for migrating from pandas or optimizing complex data workflows.

Zarr is a robust Python library designed for storing massive N-dimensional arrays. It supports efficient chunking, compression, and parallel I/O, making it ideal for large-scale scientific computing pipelines. Seamlessly integrates with NumPy, Dask, and Xarray, and supports cloud storage backends like S3 and GCS via fsspec, ensuring high performance in cloud-native environments.

A comprehensive guide detailing the process of migrating data warehouses to Snowflake from major sources like Amazon Redshift, Google BigQuery, or on-premise databases. This resource covers critical steps including schema conversion (handling dialect differences like STRUCT/ARRAY to VARIANT), robust data transfer methods (S3/Cloud Storage, Python/Pandas), data validation techniques (checksums, row counts), and essential SQL syntax translation patterns.

This powerful tool compares identical tables across two SQL Server instances (e.g., Production vs. Staging). It efficiently detects missing rows, extra columns, schema drifts, and data mismatches. Leveraging Python and Apache Arrow for fast columnar data transfer, it provides a comprehensive reconciliation report, making it essential for data migration validation and ETL verification.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.

This advanced helper assists users in writing, debugging, and validating complex formulas, pivot tables, and array functions for platforms like Excel and Google Sheets. It ensures formulas are robust by handling common edge cases (e.g., blank rows, mixed data types, time zone quirks) and provides detailed, worked examples. It also manages cross-platform dialect translation.

A spectral vector search tool that enhances nearest-neighbor search with graph Laplacian features, capturing both semantic similarity and structural roles in embedding spaces.

A powerful tool for querying individual-level genotype data from the extensive 1000 Genomes Project dataset (GRCh38). This skill allows users to identify specific genetic variants within a region or to pinpoint which individuals carry variants matching complex criteria (e.g., specific allele frequencies, clinical significance). It is essential for genetic association studies, population genetics, and assessing individual relatedness.

Access comprehensive real-time and historical market data across major global exchanges, including HK, US, A-share, and Singapore. This skill provides quotes, K-line charts, order book depth, trade ticks, market sentiment indices, and sophisticated cross-market analysis tools like ADR premium and FX carry trade frameworks. Ideal for quantitative analysis, technical trading, and monitoring global financial movements.