Dados e Análise

Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior

Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or proce

Analyze datasets to discover patterns, anomalies, and relationships. Use when exploring data files, generating statistical summaries, checking data quality, or creating visualizations. Supports CSV, Excel, JSON, Parquet, and more.

Use esta ferramenta quando o usuário desejar gerar, normalizar, verificar, deduplicar ou exportar conjuntos de dados de treinamento para Codex, Antigravity ou Claude Code a partir de tópicos, URLs, material de referência, pesquisa na web ou arquivos JSONL/CSV existentes. Suporta fluxos de trabalho SFT e DPO, esquemas de exportação personalizados e scripts de pipeline locais determinísticos.

Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use this skill when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing an

Use this skill when verifying factual claims, checking accuracy of statements, or assessing the credibility of information. Trigger phrases: ''fact-check this'', ''is this true'', ''verify this claim'', ''check if this is accurate''. Do NOT use for subjective opinions, legal or medical advice, or claims requiring real-time data or breaking news.

A template for creating new skills. Use when initializing a new skill to ensure proper structure and metadata.

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.

Use this skill when designing, cleaning, deduplicating, or documenting datasets for model training and evaluation including schema design, class imbalance handling, and train/val/test splits. Not for running model training or hyperparameter tuning. Not for real-time data pipeline engineering.

This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic fe

Use when asking about BigQuery-specific features, syntax, or capabilities including: STRUCT/ARRAY/UNNEST patterns, MERGE statements, BigQuery scripting (DECLARE, IF, LOOP, BEGIN/END), scheduled queries, remote functions, JSON functions, approximate aggregation (APPROX_COUNT_DISTINCT, HLL_COUNT), geography/GIS functions, BigQuery ML (CREATE MODEL), search indexes, vector search, or BI Engine. Trigg

This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.