Skill UI

Arboreto infers gene regulatory networks from bulk or single-cell RNA-seq data using scalable GRNBoost2 and GENIE3 algorithms, letting researchers filter transcription-factor targets, run distributed Dask jobs, and export ranked links for downstream analysis.

Connect to NCBI's GEO repository, search series/samples/platforms via Entrez queries, fetch profiles, and download raw/processed matrices using GEOparse for transcriptomics and expression analysis workflows.

Query the PrimeKG precision medicine knowledge graph to explore genes, drugs, diseases, phenotypes, and their relationships, supporting drug discovery, repurposing, and phenotype analysis through programmatic scripts.

This script performs statistical analysis on Zeek connection logs (conn.log) to detect Command and Control (C2) beaconing patterns. It leverages the ZAT library to load data into Pandas DataFrames, calculates inter-arrival time statistics, and flags connections with low standard deviation relative to the mean. This technique is crucial for network threat hunting and security incident investigation when identifying malicious, periodic callbacks.

This expert guides users through comprehensive customer research, whether analyzing existing assets (transcripts, surveys, tickets) or gathering new intel from online sources (Reddit, G2, forums). It helps uncover deep customer pains, desired outcomes, and underlying needs (Jobs to Be Done) to ground product strategy, marketing copy, and positioning in real-world evidence.

Provides programmatic and standardized access to massive, versioned datasets of single-cell and spatial transcriptomics from CZ CELLxGENE. This skill allows users to query millions of cells, analyze gene expression, and access pre-calculated embeddings across various tissues, diseases, and species without downloading entire datasets. Essential for large-scale bioinformatics analysis, machine learning model training, and comprehensive cross-dataset comparison.

Datamol is a high-level, Pythonic abstraction layer built over RDKit, designed for computational chemistry and drug discovery. It simplifies complex molecular operations, offering sensible defaults for tasks like SMILES standardization, descriptor calculation, fingerprint generation (ECFP4), 3D conformer analysis, and molecular clustering. It supports efficient parallel processing and cloud storage I/O, making it ideal for handling large-scale chemical datasets.

PyOpenMS is a powerful Python library providing bindings to the OpenMS platform for comprehensive mass spectrometry data analysis. It supports end-to-end workflows in proteomics and metabolomics, including feature detection, peptide/protein identification, quantification (label-free/isobaric), and advanced data processing. Users can leverage ready-to-run scripts for tasks like format conversion, accurate mass searching, and multi-sample quantification.

A comprehensive Python toolkit built on AnnData for analyzing single-cell RNA-seq data. It supports the complete workflow, including quality control (QC), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering, marker identification, and cell type annotation. Users benefit from a suite of ready-to-run CLI scripts that guide the entire process from raw data loading to publication-quality visualization.

This comprehensive guide teaches users how to set up a ClickHouse database environment. It covers creating a MergeTree table, inserting structured event data using TypeScript, and executing essential analytical queries (e.g., counting, grouping, unique user metrics). Ideal for beginners learning OLAP databases, testing connections, or mastering core data warehousing concepts.

A comprehensive tutor designed to build robust statistical thinking. It guides users through the entire data analysis lifecycle, from exploratory data visualization and descriptive statistics to complex hypothesis testing and regression modeling. The focus is on understanding the 'why' and 'how' of statistical logic using real datasets and practical problems, rather than just memorizing formulas. It is tool-agnostic, supporting concepts learned in Python, R, SPSS, or Stata.

Execute comprehensive, asynchronous transcription workflows using AssemblyAI. This tool handles file upload, queues jobs, and provides advanced audio intelligence features. Capabilities include speaker diarization, sentiment analysis, named entity recognition (NER), topic categorization (IAB), content safety moderation, and sensitive PII redaction. Ideal for processing meeting recordings, interviews, and large volumes of spoken content.