Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Accesses the NIH Metabolomics Workbench REST API to retrieve comprehensive metabolomics study data, metabolite structures, and standardized RefMet nomenclature.
Facilitates the development and migration of Python-based AI agents using LangGraph v1 and LangChain v1 standards.
Transcribes audio files locally using whisper.cpp with CUDA acceleration for high-performance speech-to-text conversion.
Transforms raw datasets into professional-grade charts, graphs, and visual plots using intelligent data analysis.
Optimizes complex multi-objective and constrained problems using state-of-the-art evolutionary algorithms and decision-making tools.
Accelerates reinforcement learning workflows with high-performance environment vectorization and optimized PPO training.
Performs comprehensive single-cell RNA-seq analysis workflows including quality control, clustering, and visualization.
Accesses and queries the Human Metabolome Database for detailed metabolite information, clinical biomarkers, and biochemical pathways.
Automates electronic lab notebook workflows including data uploads, notebook backups, and programmatic entry management via the LabArchives REST API.
Provides specialized support for MATLAB and GNU Octave numerical computing, matrix operations, and scientific visualization.
Processes genomic data files including SAM, BAM, VCF, and FASTA using a high-performance Python interface to htslib.
Implements production-ready reinforcement learning algorithms using a unified, scikit-learn-style API for efficient agent training and evaluation.
Accesses and analyzes comprehensive pharmaceutical data, including drug properties, interactions, molecular targets, and chemical structures from DrugBank.
Automates scientific hypothesis generation and testing from tabular datasets using LLMs and literature integration.
Connects Claude Scientific Skills with the K-Dense Web platform to handle complex, multi-agent scientific research workflows.
Explains machine learning model predictions and feature importance using Shapley Additive exPlanations for transparent AI.
Queries the ClinicalTrials.gov API v2 to search, filter, and extract comprehensive clinical research data and trial details.
Queries gene-drug interactions, clinical guidelines, and genetic variant annotations from the ClinPGx database for precision medicine applications.
Builds machine learning models and embeddings for genomic interval data to enable similarity searches, clustering, and single-cell analysis.
Performs differential gene expression analysis for bulk RNA-seq data using Python-based DESeq2 workflows.
Develops and trains Graph Neural Networks (GNNs) using the PyTorch Geometric library for irregular data structures and geometric deep learning.
Analyzes and extracts insights from images, videos, and audio files using advanced AI models.
Provides direct access to the KEGG REST API for biological pathway analysis, gene mapping, and metabolic research.
Streamlines deep learning development by organizing PyTorch code into scalable, boilerplate-free Lightning modules and automated training workflows.
Provides comprehensive tools for phylogenetic tree manipulation, evolutionary analysis, and high-quality biological data visualization.
Evaluates scientific research rigor and evidence quality using standardized frameworks like GRADE and Cochrane.
Transforms monolithic Python research code and notebooks into modular, production-ready package structures.
Builds process-based discrete-event simulations in Python to model complex systems with resource contention and time-based events.
Optimizes vector search and RAG applications through intelligent embedding model selection and advanced document chunking strategies.
Implements comprehensive audio processing and text-to-speech generation using the Google Gemini API.
Scroll for more results...