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。
Infers gene regulatory networks (GRNs) from transcriptomics data using scalable algorithms like GRNBoost2 and GENIE3.
Performs comprehensive biological computation, sequence manipulation, and NCBI database integration using the Biopython toolkit.
Provides programmatic access to standardized single-cell genomics data for high-scale querying, analysis, and machine learning integration.
Performs constraint-based metabolic modeling and systems biology analysis using the COBRApy library.
Accesses over 40 bioinformatics web services and databases through a unified Python API for biological data retrieval and analysis.
Generates advanced financial models including DCF analysis, Monte Carlo simulations, and sensitivity testing for investment valuation and risk assessment.
Builds and optimizes agentic LLM-powered data processing pipelines for unstructured document analysis and information extraction.
Diagnoses and resolves distributed training issues like hangs, OOM errors, and NCCL communication failures in LLM reasoning frameworks.
Builds and tests high-quality data visualization dashboards using the McKinsey Vizro low-code toolkit.
Solves complex linear algebra problems by calculating characteristic polynomials, eigenvalues, and eigenvectors using Sympy and Z3 verification.
Optimizes AI inference spending by intelligently routing requests to the most cost-effective model across five major providers.
Generates text and images using the Gemini Web API to provide vision-capable AI features and multi-turn conversations within Claude Code.
Enables Claude to create, analyze, and format professional Excel spreadsheets and financial models with formula-preserving capabilities.
Creates, edits, and analyzes Excel spreadsheets with production-grade formulas, industry-standard financial formatting, and automated recalculation.
Streamlines access to over 40 bioinformatics web services and databases for biological data retrieval and cross-database analysis.
Accesses the Human Metabolome Database (HMDB) to retrieve comprehensive data on small molecule metabolites, clinical biomarkers, and biochemical pathways.
Predicts high-accuracy 3D protein-ligand binding poses using state-of-the-art diffusion-based deep learning models.
Applies advanced machine learning techniques to chemistry, biology, and materials science for molecular property prediction and drug discovery.
Performs complex biological computation, sequence analysis, and bioinformatics workflows using the Biopython library.
Simplifies molecular cheminformatics workflows by providing a Pythonic wrapper for RDKit with sensible defaults and parallel processing.
Manipulates genomic datasets including SAM, BAM, VCF, and FASTA files through a Pythonic interface to htslib.
Optimizes data processing workflows with high-performance Polars expressions, lazy evaluation, and efficient DataFrame manipulations.
Enables parallel and distributed computing in Python to scale data processing beyond memory limits.
Integrates the world's most comprehensive cancer mutation database into your research workflow to query somatic mutations, signatures, and gene census data.
Generates publication-ready scientific figures and multi-panel layouts using Matplotlib, Seaborn, and Plotly.
Applies medicinal chemistry rules and structural filters to prioritize drug-like compounds in molecular discovery workflows.
Performs comprehensive differential gene expression analysis from bulk RNA-seq count data using the Python implementation of DESeq2.
Manipulates, analyzes, and visualizes phylogenetic and hierarchical trees for genomic research and evolutionary biology.
Manages and analyzes microscopy data programmatically using the OMERO Python API and data management platform.
Queries the Open Targets Platform to identify and prioritize therapeutic drug targets using human genetics, omics, and clinical evidence.
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