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。
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and advanced semantic search architectures.
Automates Electronic Lab Notebook workflows through the LabArchives REST API for research data management and backup.
Empowers researchers to generate novel hypotheses, explore interdisciplinary connections, and overcome creative blocks through collaborative ideation.
Queries the PubChem database to retrieve chemical structures, molecular properties, and bioactivity data for cheminformatics workflows.
Designs and implements evolutionary persistent memory architectures for AI agent systems using RAG and Knowledge Graphs.
Implements advanced document chunking strategies to optimize retrieval-augmented generation (RAG) performance and embedding accuracy.
Architects high-performance AI prompts using advanced complexity-based standards, attention management, and structural optimization patterns.
Implements advanced agentic patterns to prevent context saturation and optimize parallel execution within Claude Code environments.
Optimizes AI models for resource-constrained edge devices using advanced quantization, memory management, and battery-smart inference patterns.
Performs rigorous statistical analysis, hypothesis testing, and APA-compliant reporting for academic and experimental research.
Generates testable, evidence-based scientific hypotheses and experimental designs across multiple research domains.
Designs and implements autonomous AI agents with sophisticated tool use, memory systems, and multi-agent orchestration.
Architects and implements robust autonomous AI agents with sophisticated tool use, memory systems, and multi-agent orchestration.
Facilitates drug discovery and therapeutic machine learning by providing AI-ready datasets, benchmarks, and molecular evaluation oracles.
Accesses and analyzes global public statistical data through the Data Commons knowledge graph and Python API.
Evaluates and optimizes RAG system performance through comprehensive retrieval, generation, and latency metrics.
Generates production-grade Retrieval-Augmented Generation (RAG) pipeline boilerplate with industry best practices.
Enables advanced biomedical literature searches and programmatic data retrieval via the PubMed E-utilities REST API.
Streamlines genomics pipeline development and data management on the DNAnexus cloud platform using the dxpy Python SDK.
Conducts comprehensive market analysis, industry trend tracking, and market sizing calculations to drive data-informed business decisions.
Optimizes AI responses using advanced prompting techniques like chain-of-thought, few-shot learning, and structured system designs.
Architects and deploys production-grade computer vision systems including object detection, segmentation, and real-time video processing using state-of-the-art AI models.
Optimizes RAG pipeline performance by recommending tailored document chunking strategies based on content type and embedding models.
Optimizes large-scale Mixture-of-Experts (MoE) model training with enterprise-grade reinforcement learning features and low-precision quantization.
Implements production-ready architectural patterns and scalable designs for enterprise LangChain applications.
Accesses UniProt's comprehensive protein sequence and functional information resource via REST API for bioinformatics workflows.
Automates the transfer of HuggingFace models to RunPod Network Volumes via Google Colab to minimize GPU billing costs.
Standardizes machine learning experiment management using Hydra and OmegaConf configuration patterns.
Searches and retrieves life sciences preprints from the bioRxiv database with advanced filtering and PDF download capabilities.
Connects Claude to cloud laboratory services for automated protein testing, sequence optimization, and wet-lab validation.
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