Data Science & ML Claude 스킬

Functional Enrichment Analysis

Performs Gene Ontology and KEGG pathway enrichment analysis from genomic regions or gene lists with automated R-based visualizations.

ToolUniverse Scientific Workflow Suite

Enables Claude to perform complex scientific research by providing access to over 600 bioinformatics, genomics, and cheminformatics tools.

Flow Nexus Swarm Orchestrator

Deploys and manages cloud-based AI agent swarms using event-driven workflow automation and intelligent coordination.

SAP Data Intelligence Cloud Developer

Streamlines the development of data processing pipelines, ABAP integrations, and machine learning scenarios within SAP Data Intelligence Cloud.

ReasoningBank with AgentDB

Implements high-performance adaptive learning and experience replay for AI agents using AgentDB's ultra-fast vector storage.

AgentDB Memory Patterns

Implements high-performance persistent memory and learning patterns for AI agents using AgentDB.

SAP HANA Machine Learning

Streamlines in-database machine learning workflows within SAP HANA using the Python hana-ml library.

Regulatory Community Analysis for ChIA-PET

Analyzes protein-mediated chromatin interactions to identify and visualize regulatory communities from ChIA-PET datasets.

SAP AI Core & Generative AI Hub

Streamlines the deployment and management of enterprise AI/ML workloads and LLM orchestration on SAP BTP.

Flow Nexus Neural

Trains and deploys complex neural networks across distributed E2B sandbox environments directly within Claude.

ATAC-seq Quality Control

Validates the biological quality of ATAC-seq data by calculating metrics like TSS enrichment, fragment size distribution, and peak overlap.

Memory Manager for elizaOS

Optimizes AI agent memory usage and manages conversation context through advanced pruning and archiving strategies.

Known Motif Enrichment Analysis

Identifies enriched transcription factor binding motifs in genomic regions or gene lists using the HOMER bioinformatics suite.

Databricks Feature Engineering

Streamlines the creation, management, and serving of scalable feature stores within the Databricks MLOps ecosystem.

MLflow Experiment Tracking

Optimizes machine learning workflows on Databricks by implementing structured MLflow experiment tracking and model governance patterns.

ML Monitoring for Databricks

Implements automated model monitoring, drift detection, and performance tracking for production machine learning systems on Databricks.

Databricks Delta Live Tables

Build and manage declarative, self-healing data pipelines with built-in quality enforcement and automated lineage tracking.

Prompt Engineering Toolkit

Optimizes Large Language Model performance through expert prompt design, few-shot learning, and advanced reasoning patterns.

Databricks Model Serving

Deploys and manages production-grade machine learning models on Databricks with support for A/B testing and auto-scaling.

Agent Frameworks & Orchestration

Develops autonomous AI agents and multi-agent systems using industry-standard frameworks like LangChain, CrewAI, and AutoGen.

LLM Evaluation Metrics

Measures and optimizes Large Language Model performance through systematic quality frameworks, benchmarks, and hallucination detection.

LLM Fundamentals & Architecture

Simplifies Large Language Model integration by providing expert guidance on transformer architecture, tokenization, and inference optimization.

Fine-Tuning AI Models

Adapts and optimizes Large Language Models using LoRA, QLoRA, and instruction tuning for domain-specific applications.

Model Deployment

Deploys and optimizes large language models using production-grade frameworks like vLLM, TGI, and FastAPI.

Pydicom Medical Imaging

Processes DICOM medical imaging files for metadata extraction, pixel data manipulation, and secure patient data anonymization.

RAG Systems Builder

Builds production-grade Retrieval Augmented Generation pipelines with vector search, hybrid retrieval, and advanced re-ranking strategies.

Vector Database Master

Optimizes vector storage and retrieval strategies for high-performance AI applications and semantic search.

COBRApy Metabolic Modeling

Performs constraint-based reconstruction and analysis of metabolic models for systems biology and metabolic engineering research.

RAG Research & Semantic Search

Optimizes document retrieval and semantic search workflows using RAG best practices, vector databases, and advanced chunking strategies.

Incentive Prompting Optimizer

Optimizes AI outputs using research-backed prompting techniques to increase response quality and accuracy by up to 115%.