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。
Extracts taxonomy seeds and hierarchical topics from academic survey papers to bootstrap research structures.
Synthesizes structured research data into comprehensive, evidence-backed narratives for systematic reviews.
Empowers Claude to create, train, and deploy self-learning autonomous agents using nine specialized reinforcement learning algorithms.
Audits research claims against supporting evidence to identify gaps, unsupported assertions, and weak evaluation protocols.
Facilitates deep analysis and navigation of the AgentScope library to assist in building multi-agent systems.
Manages systematic literature screening by evaluating titles and abstracts against formal research protocols to generate auditable logs.
Standardizes and populates risk-of-bias assessment fields in evidence extraction tables to ensure research quality and transparency.
Calculates and interprets key financial ratios and performance metrics from corporate financial statements to support investment analysis.
Migrates codebase references, API calls, and prompts from previous Claude models to the Opus 4.5 architecture.
Generates sophisticated financial models including DCF analysis, Monte Carlo simulations, and risk assessments for investment decision-making.
Implements high-performance semantic vector search and intelligent document retrieval for Claude-powered RAG systems.
Generates structured, multi-level research taxonomies from paper sets to anchor survey outlines and content mapping.
Orchestrates a multi-model deliberation process using Fireworks AI to generate high-quality, synthesized answers through consensus and peer ranking.
Refines LaTeX academic prose by optimizing sentence rhythm, removing filler phrases, and preserving citation integrity.
Generates comprehensive ML/AI review papers using the IEEEtran LaTeX template with verified BibTeX citations and structured issue tracking.
Accesses and retrieves genomic data, including DNA/RNA sequences and raw reads, from the European Nucleotide Archive (ENA) via REST APIs and FTP.
Empowers AI agents to conduct complex scientific research by providing standardized access to over 600 specialized tools across bioinformatics, genomics, and drug discovery.
Analyzes single-cell omics data using deep generative models for batch correction, integration, and differential expression.
Processes and analyzes complex physiological signals including ECG, EEG, EDA, and respiratory data for scientific research and clinical applications.
Accesses and retrieves genomic data, sequences, and metadata from the European Nucleotide Archive via REST APIs and FTP.
Processes and analyzes complex physiological signals including ECG, EEG, EDA, and respiratory patterns for psychophysiological research.
Streamlines the implementation and management of Pinecone vector databases for production-grade AI and RAG applications.
Develops and deploys specialized machine learning models for healthcare using clinical datasets, medical coding systems, and deep learning architectures.
Simulates complex fluid dynamics using high-performance Python pseudospectral methods for Navier-Stokes and geophysical flow equations.
Simulates high-performance fluid dynamics using pseudospectral methods and Python-based HPC workflows.
Provides unified access to 20+ genomic databases and analysis methods for rapid bioinformatics research and sequence analysis.
Performs constraint-based metabolic modeling and simulation for systems biology and metabolic engineering applications.
Automates the generation, refinement, and testing of scientific hypotheses using data-driven insights and literature integration.
Generates interactive, publication-quality data visualizations and dashboards using the Plotly Python library.
Processes and analyzes complex physiological signals including ECG, EEG, and EDA using a comprehensive Python toolkit.
Scroll for more results...