data science & ml Claude 스킬을 발견하세요. 61개의 스킬을 탐색하고 AI 워크플로우에 완벽한 기능을 찾아보세요.
Manages fast, reproducible scientific Python environments by unifying conda and PyPI ecosystems within a single workflow.
Extracts and converts PDF content into clean, LLM-ready markdown or text using AI-powered and high-fidelity local tools.
Streamlines the analysis, transformation, and visualization of Excel spreadsheets using advanced Python libraries within Claude Code.
Decodes intermediate transformer layer activations into human-readable vocabulary predictions to visualize model processing.
Design and optimize high-performance Retrieval-Augmented Generation (RAG) pipelines for LLM applications.
Orchestrates a multi-stage subagent pipeline to analyze large datasets or codebases that exceed standard context windows.
Evaluates AI systems for fairness and implements mitigation strategies using demographic parity, equalized odds, and proxy detection.
Designs, evaluates, and optimizes high-performance LLM prompts using systematic engineering patterns and rigorous testing frameworks.
Refines vague data analysis requests into clear, actionable objectives through Socratic questioning and structured specification.
Provides a structured diagnostic framework for fixing broken data science workflows, notebook errors, and incorrect analysis results.
Automates the creation of scientifically accurate disease mechanism entries in the Dismech knowledge base using a research-first approach.
Manages GPU VRAM allocation through OOM retry logic, idle auto-unloading, and cross-service signaling protocols.
Refines complex AI outputs through iterative multi-model battles and consensus-driven self-critique.
Accesses and analyzes global public statistical data from the Data Commons knowledge graph for research and development.
Evaluates research rigor by assessing methodology, experimental design, and statistical validity using frameworks like GRADE and Cochrane.
Simplifies building and managing stateful AI agents with long-term memory using the Letta framework.
Performs advanced computational molecular biology tasks including sequence analysis, database queries, and structural bioinformatics.
Provides programmatic access to over 40 bioinformatics web services and databases for streamlined biological data retrieval and analysis.
Performs advanced astronomical data analysis, coordinate transformations, and cosmological calculations using the Astropy Python library.
Orchestrates iterative refinement cycles across multiple LLMs to produce high-quality, peer-reviewed outputs through competitive judging.
Performs comprehensive differential gene expression analysis on bulk RNA-seq data using the Python implementation of DESeq2.
Analyzes single-cell omics data using deep generative models and probabilistic frameworks for genomics research.
Implements Group Relative Policy Optimization (GRPO) for training language models in reasoning, logic, and structured output tasks.
Implements robust Retrieval-Augmented Generation systems using vector databases and semantic search to ground AI responses in external knowledge.
Trains autonomous AI agents using nine reinforcement learning algorithms and WASM-accelerated neural inference for rapid behavioral optimization.
Trains and deploys distributed neural networks within E2B sandboxes for scalable AI model development and orchestration.
Automates querying the Reactome pathway database for gene enrichment, molecular interactions, and systems biology research.
Streamlines the creation of distributable scientific Python packages using modern pyproject.toml standards and community-best practices.
Generates production-grade, structured system prompts by analyzing complex user requirements and defining optimal AI agent architectures.
Accesses and analyzes data from the Human Metabolome Database for metabolite identification, biomarker discovery, and clinical research.
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