LeoLin990405

Grimoire converte PDFs, documentos ou texto bruto em notas de leitura e um pacote de habilidades reutilizável em uma única análise. Ele classifica automaticamente o conteúdo, estrutura notas específicas para o Obsidian e extrai habilidades, utilizando a API MinerU sem LLMs ou escrita direta no vault.

Esta skill realiza análise e visualização de dados em R, incluindo a execução de scripts, gerenciamento de pacotes, análise estatística e criação de visualizações com ggplot2/plotly ou manipulação de dados com tidyverse.

Arquivado. As habilidades ativas de PM do Lenny agora estão em claude-code-skills, sob product-management/lenny-*.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.

Kit de ferramentas abrangente para manipulação de PDFs, permitindo extrair texto e tabelas, criar novos PDFs, mesclar/dividir documentos e gerenciar formulários. Ideal para quando Claude precisar preencher formulários ou processar, gerar e analisar documentos PDF em escala.

Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional fo

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.

Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.

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

Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.