Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Calculates NFT rarity scores and ranks tokens by trait uniqueness using multiple algorithms and real-time metadata.
Integrates local LLM inference into Go applications using the yzma library and llama.cpp.
Integrates Google's Gemini models into your Claude workflow to process multimodal inputs like images, video, and audio.
Performs structured, read-only analysis of pipeline results to diagnose model failures and map error patterns without modifying files.
Builds process-based discrete-event simulations in Python for modeling complex systems with shared resources and time-based events.
Queries over 20 genomic databases to provide instant access to gene information, sequence alignments, and protein structures.
Queries the STRING database to analyze protein-protein interaction networks and perform functional enrichment across 5,000+ species.
Enables state-of-the-art protein language modeling for sequence design, structure prediction, and functional annotation.
Builds high-performance, real-time voice applications and AI agents with low-latency communication and streaming infrastructure.
Automates the creation, editing, and professional formatting of Excel workbooks and tabular data files with an emphasis on dynamic formulas and financial standards.
Optimizes LLM performance by managing token limits and organizing context through strategic summarization, trimming, and routing.
Builds and validates Bayesian statistical models using PyMC and ArviZ for probabilistic programming and advanced inference.
Powers advanced whole-slide image analysis and machine learning workflows for computational pathology and spatial proteomics.
Automates complex quantum chemistry workflows and molecular simulations via a cloud-based Python API.
Accesses over 230 million purchasable compounds from the ZINC repository for virtual screening and cheminformatics.
Provides programmatic access to over 800,000 economic time series from the Federal Reserve Economic Data (FRED) database for advanced macroeconomic analysis and research.
Optimizes AI performance by engineering context windows through intelligent summarization, trimming, and token prioritization.
Conducts systematic, rigorous evaluations of scientific manuscripts and grant proposals across academic disciplines.
Enables parallel and distributed computing for large-scale Python data workflows that exceed available memory.
Optimizes complex multi-objective and single-objective problems using state-of-the-art evolutionary algorithms.
Evaluates scientific claims and evidence quality using rigorous frameworks like GRADE and Cochrane Risk of Bias.
Implements advanced time series machine learning algorithms including classification, forecasting, and anomaly detection with scikit-learn compatibility.
Processes and analyzes massive tabular datasets exceeding available RAM using out-of-core DataFrames and lazy evaluation.
Provides expert guidance and code generation for MATLAB and GNU Octave in numerical computing, matrix operations, and scientific data visualization.
Performs exact symbolic mathematical computations including algebra, calculus, and physics modeling within Python environments.
Optimizes high-performance data manipulation and ETL pipelines using the Polars DataFrame library with lazy evaluation and Apache Arrow.
Simulates high-performance computational fluid dynamics using Python-based pseudospectral methods and MPI parallelization.
Builds machine learning models and unsupervised embeddings for genomic interval data and single-cell chromatin accessibility datasets.
Predicts accurate protein-ligand binding poses using diffusion-based deep learning models for structure-based drug discovery.
Accesses the ClinicalTrials.gov API v2 to search, filter, and retrieve comprehensive global clinical study data for research and analysis.
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