Total 30,649 skills
Showing 12 of 30649 skills
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
A Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics. Great for exploring relationships between variables and visualizing distributions. Use for statistical data visualization, exploratory data analysis (EDA), relationship plots, distribution plots, categorical comparisons, regression visualization, heatmaps, cluster maps, and creating publication-quality statistical graphics from Pandas DataFrames.
Generate Flutter applications using Clean Architecture with feature-first structure, Riverpod state management, Dio + Retrofit for networking, and fpdart error handling. Use this skill when creating Flutter apps, implementing features with clean architecture patterns, setting up Riverpod providers, handling data with Either type for functional error handling, making HTTP requests with type-safe API clients, or structuring projects with domain/data/presentation layers. Triggers include "Flutter app", "clean architecture", "Riverpod", "feature-first", "state management", "API client", "Retrofit", "Dio", "REST API", or requests to build Flutter features with separation of concerns.
Protein Dynamics, Evolution, and Structure analysis. Specialized in Normal Mode Analysis (NMA) using Anisotropic (ANM) and Gaussian Network Models (GNM). Features tools for structural ensemble analysis, PCA, and co-evolutionary analysis (Evol). Use for protein flexibility prediction, collective motions, structural ensemble comparison, hinge region identification, binding site analysis, MD trajectory filtering, and evolutionary analysis.
Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.
Expert-level Ember.js development. Use when asked to (1) write Ember.js applications with components, services, routes, or controllers, (2) implement Ember Data models and adapters, (3) work with Ember Octane patterns (Glimmer components, tracked properties, modifiers), (4) optimize Ember application performance, (5) write Ember tests with QUnit or testing-library, or when phrases like "Ember component", "Ember route", "Glimmer", "tracked property", "Ember addon" appear.
Library for bioinformatics and community ecology statistics. Provides data structures and algorithms for sequences, alignments, phylogenetics, and diversity analysis. Essential for microbiome research and ecological data science. Use for alpha/beta diversity metrics, ordination (PCoA), phylogenetic trees, sequence manipulation (DNA/RNA/Protein), distance matrices, PERMANOVA, and community ecology analysis.
Use when errors occur deep in execution and you need to trace back to find the original trigger - systematically traces bugs backward through call stack, adding instrumentation when needed, to identify source of invalid data or incorrect behavior
Comprehensive guide for Qiskit - IBM's quantum computing framework. Use for quantum circuit design, quantum algorithms (VQE, QAOA, Grover, Shor), quantum simulation, noise modeling, quantum machine learning, and quantum chemistry calculations. Essential for quantum computing research and applications.
Comprehensive guide for Biopython - the premier Python library for computational biology and bioinformatics. Use for DNA/RNA/protein sequence analysis, file I/O (FASTA, FASTQ, GenBank, PDB), sequence alignment, BLAST searches, phylogenetic analysis, structure analysis, and NCBI database access.
Advanced sub-skill for PyTorch focused on deep research and production engineering. Covers custom Autograd functions, module hooks, advanced initialization, Distributed Data Parallel (DDP), and performance profiling.
Downloads videos from YouTube and other sites using yt-dlp. Use when downloading videos, extracting metadata, or batch downloading multiple files.