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Design sport-specific practice sessions. Drills by skill level and time, warm-up, skill work, scrimmage, cool-down.

Use when implementing any feature or bugfix, before writing implementation code

DNA methylation analysis with methylKit in R. Import Bismark coverage files, filter by coverage, normalize samples, and perform statistical comparisons. Use when analyzing single-base methylation patterns, comparing samples, or preparing data for DMR detection.

Assesses genetic health of populations for conservation with Ne estimation across time horizons (LDNe NeEstimator V2 option-file API + SNeP physical-linkage correction; recent trajectory via GONE/GONE2; deep history via Stairway Plot 2 / dadi / fastsimcoal2 / PSMC), F-statistics, runs of homozygosity binned by length class to date inbreeding, genetic-load decomposition (Bertorelle 2022 realized vs

Cria relatórios clínicos abrangentes, como relatórios de caso, diagnóstico, ensaios clínicos e documentação do paciente. Oferece suporte completo com modelos, conformidade regulatória (HIPAA, FDA, ICH-GCP) e ferramentas de validação.

Princípios de perfil de desempenho. Técnicas de medição, análise e otimização.

Modern shell prompt configuration with Powerlevel10k and Zsh Vi Mode. Use when configuring shell prompts, setting up vi/vim keybindings in zsh, customizing cursor styles per mode, adding mode indicators, optimizing prompt performance, or troubleshooting slow prompts. Covers P10k instant prompt, vi mode plugins, and cursor customization.

Quality control, filtering, normalization, and feature selection for spatial transcriptomics data. Calculate QC metrics, filter spots/cells, normalize counts, and identify highly variable genes. Use when filtering and normalizing spatial transcriptomics data.

Cancer Research (AACR) figures: resolution (300-1200 DPI), formats (EPS/TIFF/AI), hierarchical panel labels (Ai, Aii, Bi), figure/table limits, legend requirements with replicate counts.

Use PROACTIVELY when you need to create, update, configure, or validate Claude hooks for various events and integrations

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

Guia de configuração de nós com consciência operacional. Utilize ao configurar nós, compreender dependências de propriedades, determinar campos obrigatórios, selecionar níveis de detalhe de get_node ou aprender padrões de configuração comuns por tipo de nó.