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。
Automates the fine-tuning and adaptation of pre-trained machine learning models for custom datasets and specialized tasks.
Transforms vague natural language inputs into precision-engineered prompts for Claude, ChatGPT, Gemini, and other large language models.
Performs rigorous audits of LLM-powered features to ensure model currency, prompt quality, eval coverage, and observability.
Enables natural language data exploration, automated SQL generation, and interactive visualization using agent-based AI frameworks.
Optimizes high-performance rendering for datasets exceeding 100M points using Datashader and HoloViews.
Processes astronomical data using Astropy for coordinate transformations, FITS file I/O, physical units, and precise time handling.
Masters perceptual color management and accessible visual styling for scientific visualizations using Colorcet and the HoloViz ecosystem.
Manages scientific Python dependencies and reproducible environments using the pixi package manager with unified conda and PyPI support.
Analyzes and manipulates labeled multidimensional scientific datasets using the Xarray library and its ecosystem.
Generates interactive, publication-quality data visualizations using the HoloViz ecosystem and hvPlot for rapid data analysis.
Builds type-safe, declarative configuration systems and reactive Python applications using the HoloViz Param library.
Builds and publishes standards-compliant scientific Python packages using pyproject.toml and Hatchling.
Builds interactive geographic maps and performs spatial data analysis using GeoViews and GeoPandas.
Simplifies the creation of complex, interactive, and multi-dimensional data visualizations using the HoloViz ecosystem.
Performs transcription factor footprinting and differential binding detection on ATAC-seq data using the TOBIAS framework.
Identifies and calls chromatin loops from Hi-C data files in .mcool, .cool, or .hic formats for genomic visualization and analysis.
Boosts AI response quality by up to 115% using research-backed techniques like persona assignment, stakes language, and step-by-step reasoning.
Generates normalized BigWig signal tracks from BAM files for ATAC-seq and ChIP-seq visualization.
Identifies enriched transcription factor binding motifs in genomic regions or gene lists using the HOMER bioinformatics suite.
Analyzes protein-mediated chromatin interactions to identify and visualize regulatory communities from ChIA-PET datasets.
Performs Gene Ontology and KEGG pathway enrichment analysis from genomic regions or gene lists with automated R-based visualizations.
Performs specialized biological validation for ChIP-seq data by calculating cross-correlation metrics and fraction of reads in peaks.
Analyzes and constructs Ramanujan graphs to achieve optimal spectral expansion and network efficiency within graph-based systems.
Manages complex n-ary skill interactions and topos unification using dendroidal Segal spaces and lazy ACSet materialization.
Extracts behavioral patterns and trains learning agents for cognitive surrogate systems using temporal and topic analysis.
Optimizes interaction sequences using information theory and active inference to maximize learning efficiency and information gain.
Automates the end-to-end scientific research lifecycle from initial data analysis and hypothesis generation to the production of publication-ready LaTeX manuscripts.
Manages annotated data matrices for single-cell genomics and large-scale biological datasets using the Python AnnData framework.
Implements a cognitive immune framework using active inference and information geometry for robust system self-maintenance.
Builds robust Retrieval-Augmented Generation (RAG) systems using vector databases and semantic search to ground AI responses in proprietary data.
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