data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Facilitates breakthrough problem-solving by applying first-principles reasoning and cross-domain analogies to overcome complex technical bottlenecks.
Provides PhD-level guidance for selecting, designing, and validating academic and scientific research methodologies.
Integrates AI-powered qualitative analysis into Python pandas workflows for intelligent data sorting, deduplication, and merging.
Facilitates PhD-level academic research by managing specialized methodology skills and enforcing rigorous scientific standards.
Provides rigorous, PhD-level critiques of academic manuscripts and research methodologies to ensure high-impact scholarly standards.
Profiles and optimizes Python code to identify bottlenecks, reduce latency, and minimize memory consumption using industry-standard tools.
Orchestrates task delegation to external LLM services by offloading high-token execution while maintaining central reasoning within Claude.
Optimizes AI prompts using research-backed frameworks and production-ready templates to ensure high-quality, cost-effective model outputs.
Implements robust pipes-and-filters architectures for complex ETL, media processing, and data transformation workloads.
Designs, optimizes, and deploys scalable large language model architectures and high-performance RAG systems.
Optimizes LLM context windows by implementing sophisticated trimming, summarization, and memory management strategies for AI agents.
Build, train, and optimize high-performance neural networks using the PyTorch deep learning framework.
Simplifies LLM interactions by providing a unified Python interface for 100+ AI providers with consistent OpenAI-format syntax.
Enables memory-efficient fine-tuning of large language models using 4-bit quantization and LoRA adapters.
Configures and manages local LLM inference using Mozilla Llamafile to provide offline, OpenAI-compatible AI capabilities.
Implements parameter-efficient fine-tuning using Low-Rank Adaptation (LoRA) to specialize large language models with minimal resource overhead.
Optimizes agent behavior by automatically identifying the active LLM and adjusting execution configurations for maximum cross-model compatibility.
Optimizes ML data workflows using Polars, Arrow, and ClickHouse for high-performance, memory-efficient pipeline development.
Searches the PubMed biomedical database using natural language queries and semantic retrieval for full-text literature access.
Searches the medRxiv database for medical and health sciences preprints using natural language semantic queries.
Performs semantic natural language searches across the comprehensive DrugBank database for drug mechanisms, interactions, and pharmacology data.
Accelerates pharmaceutical research by enabling semantic natural language search across ChEMBL, DrugBank, Open Targets, and FDA drug labels.
Implements validation patterns to catch machine learning errors early and prevent wasted GPU resources during long-running experiments.
Performs semantic, natural language searches across the complete arXiv preprint archive for physics, math, and computer science research.
Conducts comprehensive, natural language semantic searches across major biomedical databases like PubMed, ClinicalTrials.gov, and FDA labels.
Searches the official FDA drug labels database using natural language queries to retrieve prescribing information, safety data, and dosing guidelines.
Performs natural language semantic searches across the Open Targets database for drug-disease associations and target validation.
Enables natural language searching of ClinicalTrials.gov to find study outcomes, recruitment status, and medical intervention data.
Enables natural language semantic search across the complete bioRxiv database of biological science preprints.
Searches the ChEMBL database of bioactive molecules and drug targets using natural language queries.
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