发现data science & ml类别的 Claude 技能。浏览 53 个技能,找到适合您 AI 工作流程的完美功能。
Processes digital pathology whole slide images (WSI) through automated tissue detection, tile extraction, and specialized image preprocessing.
Automates the end-to-end scientific research lifecycle from data-driven hypothesis generation to the production of publication-ready LaTeX manuscripts.
Accelerates reinforcement learning workflows through high-performance training, optimized environment vectorization, and seamless multi-agent integration.
Explains machine learning model predictions and feature importance using Shapley values for improved transparency and debugging.
Implements reinforcement learning workflows including agent training, custom environment design, and parallelized experimentation using the Stable Baselines3 library.
Processes and analyzes physiological signals like ECG, EEG, and EDA to extract research-grade biometrics and psychophysiological insights.
Analyzes whole-slide pathology images and multiparametric imaging data using advanced machine learning, spatial graph construction, and automated preprocessing pipelines.
Models complex discrete-event systems where entities interact with shared resources over time using process-based Python simulations.
Conducts automated exploratory data analysis on over 200 scientific file formats to extract metadata, assess quality, and generate comprehensive documentation reports.
Implements, fine-tunes, and deploys pre-trained transformer models for natural language processing, computer vision, and audio tasks.
Executes exact symbolic mathematics in Python to solve equations, perform calculus, and manipulate algebraic expressions with mathematical precision.
Facilitates advanced astronomical research and data analysis using Python for coordinate transformations, FITS file manipulation, and cosmological calculations.
Detects system hardware capabilities and generates strategic recommendations for optimized scientific computing and data processing tasks.
Generates and designs novel proteins using evolutionary scale language models for sequence, structure, and functional analysis.
Manages biological datasets with end-to-end lineage tracking, ontology-based curation, and FAIR data principles using a unified Python API.
Designs, simulates, and executes quantum circuits across diverse hardware backends and simulators using Google's open-source framework.
Analyzes single-cell genomics data using probabilistic deep generative models for tasks like batch correction, cell type annotation, and multi-omic integration.
Generates and tests scientific hypotheses from observational data and research literature to accelerate empirical discovery and predictive modeling.
Executes complex autonomous biomedical research tasks across genomics, drug discovery, and clinical analysis using integrated databases and code execution.
Facilitates machine learning on genomic interval data by training embeddings for regions, single-cell ATAC-seq, and associated metadata.
Integrates Google's Gemini AI models directly into development workflows for advanced reasoning and code analysis.
Designs and implements personalized recommendation engines using collaborative filtering, content-based filtering, and hybrid modeling techniques.
Analyzes text data to identify emotional tone and classify sentiment as positive, negative, or neutral.
Automates the division of datasets into training, validation, and testing subsets for machine learning workflows.
Builds and deploys production-ready generative AI agents leveraging Google Cloud's Vertex AI platform and Gemini models.
Refines and streamlines LLM prompts to minimize token consumption, reduce operational costs, and maximize response quality.
Analyzes text data to identify emotional tone and classify sentiment as positive, negative, or neutral.
Automates the transition of machine learning models into production environments through optimized deployment workflows and API serving.
Generates informative and visually appealing charts, plots, and graphs through intelligent data analysis and automated selection of optimal visualization types.
Executes and visualizes machine learning clustering algorithms to identify groups and structures within provided datasets.
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