data science & ml Claude 스킬을 발견하세요. 53개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Builds robust Retrieval-Augmented Generation systems using vector databases, semantic search, and optimized retrieval pipelines.
Queries the Ensembl REST API to retrieve genomic annotations, sequences, variants, and comparative genomics data for over 250 species.
Accesses the world's largest chemical database to retrieve compound properties, structures, and bioactivity data for cheminformatics workflows.
Predicts high-accuracy 3D binding poses for protein-ligand complexes using diffusion-based deep learning models.
Facilitates the retrieval and analysis of over 200 million AI-predicted protein structures from the AlphaFold DB for biological research and drug discovery.
Processes and analyzes mass spectrometry data using Python-based spectral similarity and metadata harmonization.
Processes mass spectrometry data for proteomics and metabolomics analysis using the pyOpenMS library.
Architects sophisticated LLM applications using the LangChain framework with support for autonomous agents, memory management, and RAG patterns.
Streamlines genomics data analysis and pipeline development on the DNAnexus cloud platform using the dxpy SDK and CLI tools.
Automatically analyzes CSV files to generate comprehensive statistical summaries and tailored visualizations without requiring user prompts.
Analyzes and visualizes high-throughput sequencing data for genomics research and quality control.
Enables advanced molecular modeling, chemical property calculation, and structural analysis within Python workflows.
Parses and manipulates Flow Cytometry Standard (FCS) files to extract event data as NumPy arrays and manage metadata.
Automates complex Excel data processing, visualization, and formatting using powerful Python libraries like Pandas and OpenPyXL.
Accesses the ZINC database of 230M+ purchasable compounds for drug discovery, virtual screening, and molecular analog searching.
Evaluates scientific rigor by assessing research methodology, statistical validity, and potential biases using industry-standard frameworks.
Accesses the world's largest somatic mutation database for cancer research and precision oncology data retrieval.
Generates testable, evidence-based scientific hypotheses and experimental designs from observations or literature.
Queries and retrieves genomic data from NCBI Gene databases using E-utilities and the modern Datasets API.
Accesses the European Nucleotide Archive to retrieve genomic sequences, raw reads, and metadata for bioinformatics pipelines.
Generates publication-quality scientific figures and multi-panel layouts compliant with major journal standards.
Simplifies the development and training of Graph Neural Networks (GNNs) for deep learning on irregular and relational data structures.
Facilitates direct access to PubMed literature and the NCBI E-utilities API for advanced biomedical research and data extraction.
Implements advanced prompting strategies like Chain-of-Thought and few-shot learning to optimize LLM performance and output reliability.
Queries and interprets NCBI ClinVar data to evaluate human genetic variants and their clinical significance.
Optimizes data processing workflows using the high-performance Polars DataFrame library and expression API.
Implements comprehensive machine learning workflows including classification, regression, and data preprocessing using the industry-standard Scikit-learn library.
Performs rigorous statistical modeling, econometric analysis, and hypothesis testing using Python's statsmodels library.
Generates publication-quality statistical graphics and complex multi-panel data visualizations using the Seaborn Python library.
Manages large-scale N-dimensional arrays with chunking and compression for high-performance scientific computing and cloud storage.
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