data science & ml向けのClaudeスキルを発見してください。61個のスキルを閲覧し、AIワークフローに最適な機能を見つけましょう。
Conducts systematic, multi-database academic literature reviews with automated citation verification and professional formatting.
Automates the collection of authoritative deep learning resources and generates structured, multi-stage learning paths with interactive HTML guides.
Accesses and analyzes over 200 million AI-predicted protein structures from the AlphaFold DB for structural biology and drug discovery.
Enables development and training of Graph Neural Networks (GNNs) using the PyTorch Geometric library.
Solves complex single, multi, and many-objective optimization problems using evolutionary algorithms and Pareto-optimal analysis.
Searches and retrieves life sciences preprints from the bioRxiv database with advanced filtering and PDF download capabilities.
Queries the Reactome database to perform biological pathway analysis, gene-pathway mapping, and expression enrichment for systems biology research.
Streamlines the development, validation, and systematic documentation of trading strategies and market edges.
Connects Claude to cloud laboratory services for automated protein testing, sequence optimization, and wet-lab validation.
Builds and deploys serverless bioinformatics workflows using the Latch SDK and cloud infrastructure.
Implements advanced time series machine learning workflows for classification, forecasting, and anomaly detection using scikit-learn compatible APIs.
Diagnoses and resolves machine learning training failures like loss divergence and gradient issues through automated artifact analysis.
Facilitates direct integration with the Google Gemini API for multi-modal content handling and context management within multi-model infrastructures.
Streamlines genomics pipeline development and data management on the DNAnexus cloud platform using the dxpy Python SDK.
Generates interactive, publication-quality scientific and statistical charts using the Plotly Python library.
Diagnoses semantic collapse and optimizes RAG architectures using hierarchical taxonomies and Graph-RAG schemas.
Detects hardware resources and provides strategic recommendations for optimal scientific computing and data processing.
Orchestrates multi-agent AI swarms with dynamic topologies and parallel execution for complex distributed tasks.
Implements a systematic methodology for diagnosing, refining, and validating trading strategies to improve win rates and returns.
Accesses and analyzes global public statistical data through the Data Commons knowledge graph and Python API.
Enhances AI agents with high-performance adaptive learning and vector-based memory distillation using AgentDB.
Enables direct REST API access to UniProt for protein searching, FASTA sequence retrieval, and cross-database identifier mapping.
Accesses the STRING database to analyze protein-protein interaction networks and perform functional enrichment for systems biology.
Generates testable, evidence-based scientific hypotheses and experimental designs across multiple research domains.
Performs high-performance genomic interval analysis, overlap detection, and machine learning tokenization using Rust-powered tools.
Enables programmatic PDF manipulation, data extraction, and document creation using professional Python and command-line tools.
Automates professional Excel spreadsheet creation, financial modeling, and data analysis with error-free formula verification.
Automates end-to-end scientific research workflows from initial data analysis and hypothesis generation to publication-ready LaTeX manuscripts.
Streamlines the development, testing, and deployment of large-scale Ray Data jobs for distributed ML workloads.
Provides a unified interface for rapid bioinformatics queries across 20+ genomic databases including Ensembl, AlphaFold, and NCBI.
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