Discover Agent Skills for data science & ml. Browse 61 skills for Claude, ChatGPT & Codex.
Simplifies molecular machine learning workflows for drug discovery, property prediction, and materials science using DeepChem's specialized toolsets.
Develops and optimizes quantum machine learning models and circuits across multiple hardware platforms using automatic differentiation.
Processes and visualizes high-throughput sequencing data to generate publication-quality genomic insights and quality control reports.
Performs comprehensive statistical hypothesis testing, regression analysis, and Bayesian modeling with automated assumption checking and APA-style reporting.
Implements and optimizes reinforcement learning workflows using Stable Baselines3 to train reliable RL agents and design custom Gym environments.
Evaluates scientific research rigor and methodology through systematic analysis of experimental design, statistical validity, and potential biases.
Simplifies the development of AI-powered features and autonomous agents using the Vercel AI SDK.
Facilitates advanced materials analysis, crystal structure manipulation, and computational workflows using the Python Materials Genomics library.
Processes and analyzes massive tabular datasets with billions of rows using out-of-core DataFrame operations and lazy evaluation.
Optimizes Python data structures using defaultdict for efficient grouping, adjacency lists, and nested dictionary management.
Integrates with the NIH Metabolomics Workbench REST API to query metabolite data, standardized nomenclature, and experimental studies for biomarker discovery.
Implements high-performance priority queues for pathfinding, scheduling, and stream processing using efficient heap-based structures.
Accesses the ClinicalTrials.gov API v2 to search, filter, and export global clinical research data for medical analysis.
Implements efficient counting and frequency analysis patterns using Python's collections.Counter for data processing and distribution analysis.
Solves complex logic puzzles and scheduling problems using Peter Norvig's propagate-then-search algorithmic pattern.
Facilitates collaborative research ideation by generating hypotheses, identifying research gaps, and exploring interdisciplinary connections for scientific problem-solving.
Provides comprehensive cheminformatics capabilities for molecular analysis, manipulation, and drug discovery workflows.
Manages local Ollama LLM models for development, testing, and VRAM optimization within Claude Code workflows.
Accesses the Ensembl REST API to retrieve genomic sequences, gene annotations, and variant data for over 250 species.
Implements elegant, idiomatic data transformations using Pythonic list, dictionary, and set comprehensions inspired by Peter Norvig.
Analyzes Civitai video engagement and content metrics through natural language SQL queries and automated reporting.
Systematically evaluates research papers, literature reviews, and academic methodologies using the ScholarEval framework to ensure scientific rigor and novelty.
Implements memory-efficient sparse data structures using Python sets for infinite grids and large coordinate spaces.
Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.
Implements immutable, memory-efficient data structures using Python's namedtuple for cleaner and more readable code.
Accesses and analyzes over 200 million AI-predicted protein structures from the AlphaFold DB for structural biology and drug discovery.
Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.
Solves complex single, multi, and many-objective optimization problems using evolutionary algorithms and Pareto-optimal analysis.
Optimizes Python functions by implementing memoization and dynamic programming patterns to eliminate redundant recursive computations.
Queries the Reactome database to perform biological pathway analysis, gene-pathway mapping, and expression enrichment for systems biology research.
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