Explore skills

Use the Hugging Face Hub CLI (`hf`) to download, upload, and manage models, datasets, and Spaces.

Query Hugging Face datasets through the Dataset Viewer API for splits, rows, search, filters, and parquet links.

Coleta e consulta dados de leiloeiros oficiais de todas as 27 Juntas Comerciais do Brasil. Scraper multi-UF, banco SQLite, API FastAPI e exportacao CSV/JSON.

Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.

Surfaces Monte Carlo data observability context (table health, alerts, lineage, blast radius) before SQL/dbt edits.

Expert guidance for distributed NoSQL databases (Cassandra, DynamoDB). Focuses on mental models, query-first modeling, single-table design, and avoiding hot partitions in high-scale systems.

A multi-objective optimization framework featuring NSGA-II, NSGA-III, MOEA/D, Pareto fronts, and constraint handling. It includes benchmarks like ZDT and DTLZ for engineering design and optimization problems.

Parses FCS (Flow Cytometry Standard) files v2.0-3.1. Extracts events as NumPy arrays, reads metadata/channels, and converts to CSV/DataFrame for flow cytometry data preprocessing.

Medicinal chemistry filters for compound triage. Apply drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.

This skill provides structured manuscript and grant review using checklist-based evaluation, ideal for formal peer reviews assessing methodology, statistical validity, and reporting standards compliance with constructive feedback.

Formulates structured, testable hypotheses from experimental observations or data, including predictions, proposed mechanisms, and experimental designs, following the scientific method.

Infers gene regulatory networks (GRNs) from gene expression data using scalable algorithms (GRNBoost2, GENIE3). It's used to analyze transcriptomics data (bulk/single-cell RNA-seq) to identify transcription factor-target gene relationships and regulatory interactions, supporting distributed computation for large datasets.