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SDK de Compreensão de Conteúdo Azure AI para Python, usado para extração multimodal de conteúdo de documentos, imagens, áudio e vídeo.

Guide for using direnv - a shell extension for loading directory-specific environment variables. Use when setting up project environments, creating .envrc files, configuring per-project environment variables, integrating with Python/Node/Ruby/Go layouts, working with Nix flakes, or troubleshooting environment loading issues on macOS and Linux.

Predict RNA secondary structure, MFE folding, base-pair probabilities, RNA-RNA interactions via ViennaRNA Python bindings. Pipeline: sequence → MFE → partition function and pair-probability matrix → dot-bracket → duplex. Use for siRNA/sgRNA targeting, ribozyme design, RNA accessibility. Use RNAfold CLI for batch use without Python.

Construa pacotes Python da maneira correta — uma filosofia inspirada em R para scaffolding, design de API, testes, documentação, ciclo de vida de depreciação e lançamento no PyPI usando uv, rich, pytest e mkdocs-material. Ative ao criar/estruturar pacotes Python, projetar APIs, escrever testes, configurar documentação ou publicar no PyPI.

Select restriction enzymes by criteria using Biopython Bio.Restriction. Find enzymes that cut once, don't cut, produce specific overhangs, are commercially available, or have compatible ends for cloning. Use when selecting restriction enzymes for cloning or analysis.

Test Temporal workflows with pytest, time-skipping, and mocking strategies. Covers unit testing, integration testing, replay testing, and local development setup. Use when implementing Temporal workflow tests or debugging test failures.

Python library for healthcare ML on EHR data: process MIMIC-III/IV, eICU, OMOP-CDM; encode medical codes (ICD, ATC, NDC); build patient-level datasets; train Transformer, RETAIN, GRASP, MedBERT for mortality, drug recommendation, readmission, diagnosis prediction. Alternatives: FIDDLE (preprocessing), clinical-longformer (clinical NLP), ehr-ml (embeddings).

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

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.

DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.

Generate images, videos, speech audio, and music using the PonyFlash Python SDK. Also handle local media editing with FFmpeg, including clip, concat, transcode, extract audio, frame capture, subtitle capability checks, and ASS subtitle prep. Use when the user asks to create, generate, produce, edit, trim, merge, concatenate, transcode, subtitle, or render AI-generated media content.

Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.