发现data science & ml类别的 Claude 技能。浏览 61 个技能,找到适合您 AI 工作流程的完美功能。
Enables advanced protein engineering through generative design, structure prediction, and high-performance embeddings using ESM3 and ESM C models.
Predicts 3D protein-ligand binding poses using state-of-the-art diffusion models for structure-based drug discovery.
Simplifies molecular cheminformatics and drug discovery workflows using a Pythonic wrapper for RDKit.
Automates complex biomedical research tasks including genomics, drug discovery, and clinical analysis through autonomous reasoning and code execution.
Processes and prepares gigapixel whole slide images for digital pathology and machine learning workflows.
Builds complex discrete-event simulations in Python for modeling processes, resource contention, and time-based systems.
Converts diverse file formats including PDF, Office docs, and media into clean, LLM-optimized Markdown.
Streamlines computational molecular biology tasks including sequence analysis, NCBI database queries, and structural bioinformatics using the Biopython toolkit.
Automates complex biomedical research tasks including genomic analysis, drug discovery, and clinical interpretation using an autonomous agent framework.
Processes and analyzes comprehensive physiological signals including ECG, EEG, and EDA for psychophysiology and clinical research.
Integrates and manages Pinecone vector databases for production-grade AI applications and low-latency semantic search.
Automates laboratory workflows by controlling liquid handling robots, plate readers, and analytical equipment through a hardware-agnostic Python interface.
Transforms, cleans, and reshapes complex datasets locally using industry-standard Python libraries like pandas and numpy.
Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering.
Automates scientific hypothesis generation and testing by combining observational data with literature-based insights using large language models.
Analyzes single-cell omics data using the scvi-tools framework for probabilistic modeling and batch correction.
Enables advanced protein engineering, sequence generation, and structure prediction using Evolutionary Scale Modeling.
Integrates Google's Gemini models into your terminal workflow for advanced code analysis and complex multi-model reasoning.
Simplifies the creation, manipulation, and analysis of complex networks and graph data structures in Python.
Queries and analyzes SEC filings and financial statements for deep financial research and company data extraction.
Facilitates mass spectrometry data analysis using the pyOpenMS library for proteomics and metabolomics workflows.
Accesses and analyzes gene expression and functional genomics data from the NCBI Gene Expression Omnibus repository.
Processes digital pathology whole slide images by automating tissue detection, tile extraction, and preprocessing for machine learning pipelines.
Accelerates genomic interval analysis and machine learning preprocessing using a high-performance Rust-based toolkit with Python bindings.
Automates complex biomedical research tasks including genomics, drug discovery, and clinical data analysis using an autonomous AI agent framework.
Accesses and queries the NCBI Gene database to retrieve comprehensive genetic information, sequences, and functional annotations via E-utilities and Datasets APIs.
Processes and generates audio, video, images, and complex documents using Google Gemini's advanced multimodal API capabilities.
Infers gene regulatory networks from transcriptomics data using scalable gradient boosting and random forest algorithms.
Provides high-performance tools for genomic interval analysis, overlap detection, and machine learning preprocessing using Rust and Python.
Performs constraint-based reconstruction and analysis of metabolic models using Python for systems biology and metabolic engineering.
Scroll for more results...