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。
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.
Manages GPU VRAM allocation through OOM retry logic, idle auto-unloading, and cross-service signaling protocols.
Accesses and analyzes global public statistical data from the Data Commons knowledge graph for research and development.
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.
Evaluates research rigor by assessing methodology, experimental design, and statistical validity using frameworks like GRADE and Cochrane.
Guides developers in choosing the optimal neural network architecture based on data modality, problem constraints, and performance requirements.
Performs advanced computational molecular biology tasks including sequence analysis, database queries, and structural bioinformatics.
Routes AI and machine learning tasks to specialized Yzmir engineering packs based on specific project requirements and technical domains.
Simplifies building and managing stateful AI agents with long-term memory using the Letta framework.
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.
Accesses and analyzes data from the Human Metabolome Database for metabolite identification, biomarker discovery, and clinical research.
Performs comprehensive differential gene expression analysis on bulk RNA-seq data using the Python implementation of DESeq2.
Diagnoses machine learning training issues and routes users to specific optimization strategies based on model symptoms.
Optimizes large-scale deep learning workflows using Fully Sharded Data Parallel (FSDP) techniques in PyTorch.
Accesses and retrieves gene expression data from the NCBI Gene Expression Omnibus (GEO) for advanced transcriptomics and genomic analysis.
Performs systematic, objective technical analysis of weekly price charts to identify trends, support levels, and probabilistic price scenarios.
Generates professional-grade scientific plots and data visualizations using Python's foundational plotting library.
Automates querying the Reactome pathway database for gene enrichment, molecular interactions, and systems biology research.
Provides comprehensive molecular analysis and manipulation capabilities for cheminformatics and drug discovery workflows.
Streamlines the creation of distributable scientific Python packages using modern pyproject.toml standards and community-best practices.
Processes, filters, and analyzes mass spectrometry data using the matchms Python library for metabolomics and chemical discovery.
Automates end-to-end scientific research workflows from initial data analysis and hypothesis generation to producing publication-ready LaTeX manuscripts.
Builds production-grade multi-agent systems, AgentOS runtimes, and complex agentic workflows with native MCP integration.
Synchronizes MetaTrader 5 and Python environments to automate market data exports and translate MQL5 indicators into validated Python code.
Streamlines the development of type-safe AI agents using the Pydantic AI framework for Python.
Automates scientific hypothesis generation and testing by synthesizing observational data with research literature using LLMs.
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