Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Provides a comprehensive suite of 100+ molecular featurizers for converting chemical structures into machine learning-ready numerical representations.
Integrates LangChain4j into Spring Boot applications using auto-configuration and declarative AI Services.
Manages annotated data matrices for single-cell genomics and large-scale biological datasets using the AnnData framework.
Accesses and queries the Catalogue of Somatic Mutations in Cancer (COSMIC) for precision oncology and genomic research.
Scales Python, pandas, and NumPy workflows across multiple cores or clusters for larger-than-memory datasets.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and advanced semantic search architectures.
Designs and implements evolutionary persistent memory architectures for AI agent systems using RAG and Knowledge Graphs.
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.
Facilitates advanced materials science analysis and computational workflows by manipulating crystal structures, phase diagrams, and electronic data.
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.
Implements robust Retrieval-Augmented Generation (RAG) systems using the LangChain4j framework to enhance Java-based AI applications with external knowledge.
Generates production-grade Retrieval-Augmented Generation (RAG) pipeline boilerplate with industry best practices.
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.
Solves complex single and multi-objective optimization problems using state-of-the-art evolutionary algorithms and visualization tools.
Optimizes RAG pipeline performance by recommending tailored document chunking strategies based on content type and embedding models.
Analyzes and optimizes Retrieval-Augmented Generation (RAG) pipelines for performance, accuracy, and production readiness.
Optimizes large-scale Mixture-of-Experts (MoE) model training with enterprise-grade reinforcement learning features and low-precision quantization.
Optimizes AI responses using advanced prompting techniques like chain-of-thought, few-shot learning, and structured system designs.
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.
Implements production-ready architectural patterns and scalable designs for enterprise LangChain applications.
Generates professional, publication-quality statistical graphics and data visualizations directly from Python DataFrames.
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.
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