Performance
Performance profiling and optimization
ai-agents-architect
by ngxtm
"Expert in designing and building autonomous AI agents. Masters tool use, memory systems, planning strategies, and multi-agent orchestration. Use when: build agent, AI agent, autonomous agent, tool ..."
agent-orchestration-multi-agent-optimize
by ngxtm
"Optimize multi-agent systems with coordinated profiling, workload distribution, and cost-aware orchestration. Use when improving agent performance, throughput, or reliability."
performance-optimization
by miles990
Profiling, optimization techniques, and performance best practices
mobile
by miles990
Mobile development with React Native, Flutter, and native patterns
dask
by tondevrel
A flexible library for parallel computing in Python. It scales Python libraries like NumPy, pandas, and scikit-learn to multi-core systems or distributed clusters. Features lazy evaluation and task scheduling for data that exceeds RAM capacity. Use for out-of-core computing, parallel processing, distributed computing, large-scale data analysis, dask.array, dask.dataframe, dask.delayed, dask.bag, task scheduling, lazy evaluation, and scaling beyond memory limits.
pennylane
by tondevrel
Cross-platform Python library for differentiable quantum computing. Integrated with machine learning libraries like PyTorch, TensorFlow, and JAX. Designed for quantum machine learning (QML), variational algorithms, and hardware-agnostic quantum programming. Use for Quantum Neural Networks (QNNs), Variational Quantum Algorithms (VQE, QAOA), hybrid classical-quantum machine learning, quantum chemistry calculations, benchmarking quantum algorithms, optimizing quantum control pulses, and investigating QML phenomena like Barren Plateaus.
pandas-performance
by tondevrel
Advanced sub-skill for pandas focused on memory optimization, execution speed, and handling large-scale datasets (10M+ rows). Covers low-level dtypes, efficient indexing, and vectorization of complex logic.
pytorch-research
by tondevrel
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.
pmf-survey
by wdavidturner
Use when asked to "PMF survey", "measure product-market fit", "40% rule", "Sean Ellis test", "Rahul Vohra method", or "how disappointed would you be". Helps quantify product-market fit and systematically improve it. The PMF Survey framework (created by Sean Ellis, popularized by Rahul Vohra at Superhuman) measures how disappointed users would be without your product and turns that data into a roadmap.
shape-up
by wdavidturner
Use when asked to "shape up", "run a shaping session", "set an appetite", "scope a project without estimates", "betting table", or "ship in fixed cycles". Helps teams escape estimate-driven development and Scrum fatigue. The Shape Up method (created by Ryan Singer at Basecamp/37signals) uses fixed time boxes, variable scope, and collaborative shaping to ship meaningful work predictably.
matplotlib
by tondevrel
The foundational library for creating static, animated, and interactive visualizations in Python. Highly customizable and the industry standard for publication-quality figures. Use for 2D plotting, scientific data visualization, heatmaps, contours, vector fields, multi-panel figures, LaTeX-formatted plots, custom visualization tools, and plotting from NumPy arrays or Pandas DataFrames.
matplotlib-pro
by tondevrel
Professional sub-skill for Matplotlib focused on high-performance animations, complex multi-figure layouts (GridSpec), interactive widgets, and publication-ready typography (LaTeX/PGF).
numpy
by tondevrel
Comprehensive guide for NumPy - the fundamental package for scientific computing in Python. Use for array operations, linear algebra, random number generation, Fourier transforms, mathematical functions, and high-performance numerical computing. Foundation for SciPy, pandas, scikit-learn, and all scientific Python.
qutip
by tondevrel
Quantum Toolbox in Python. Framework for simulating the dynamics of open quantum systems. Provides data structures for quantum objects (kets, bras, operators) and solvers for master equations, Monte Carlo trajectories, and time-dependent Hamiltonians. Use for quantum dynamics simulation, open quantum systems, master equations, quantum optics, cavity QED, Jaynes-Cummings model, Rabi oscillations, Wigner functions, quantum correlations, entanglement analysis, and quantum control.
networkx
by tondevrel
Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Supports various graph types (Directed, Undirected, Multigraphs) and features a vast library of standard graph algorithms. Use for network analysis, graph theory, social network analysis, biological networks, infrastructure networks, path finding, centrality measures, community detection, graph algorithms, shortest paths, PageRank, connectivity analysis, and routing optimization.
prody
by tondevrel
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.
dask-optimization
by tondevrel
Advanced sub-skill for Dask focused on distributed system performance, memory management, and task graph optimization. Covers cluster tuning, efficient serialization, data skew mitigation, and dashboard-driven debugging.
pyproj
by tondevrel
Python interface to PROJ (cartographic projections and coordinate transformations library). Handles transformations between different Coordinate Reference Systems (CRS) and performs geodetic calculations (distance, area on ellipsoids). Use for coordinate transformations, CRS conversions, geodetic calculations, UTM projections, GPS coordinate conversions, ellipsoidal distance calculations, and spatial reference system operations.
angular-best-practices-legacy
by develite98
Angular 12-16 performance optimization guidelines with NgModules, RxJS patterns, and classic template syntax (*ngIf, *ngFor). Use when maintaining or working with pre-v17 Angular codebases. Triggers on tasks involving Angular components, services, modules, or performance improvements in legacy projects.
ase
by tondevrel
Atomic Simulation Environment - a set of tools for setting up, manipulating, running, visualizing, and analyzing atomistic simulations. Acts as a universal interface between Python and numerous quantum chemical and molecular dynamics codes. Use for building atomic structures, geometry optimization, molecular dynamics simulations, transition state searches (NEB), file format conversion (CIF, XYZ, POSCAR, PDB), electronic property calculations (DOS, band structures), and automating simulation workflows with DFT/MD codes like VASP, GPAW, Quantum ESPRESSO, LAMMPS.
pytorch
by tondevrel
Leading deep learning framework. Provides Tensors and Dynamic Computational Graphs with strong GPU acceleration. Widely used for research, neural networks, and differentiable programming.
pyscf
by tondevrel
Comprehensive guide for PySCF - Python-based Simulations of Chemistry Framework. Use for ab initio quantum chemistry calculations including Hartree-Fock, DFT, MP2, CCSD, geometry optimization, excited states, and molecular properties. Industry-standard library for electronic structure calculations.
swift-quiz
by ITlearning
Adaptive Swift/iOS quiz with two modes. Classic is lighter, Mastery requires mechanism-level reasoning.
h5py
by tondevrel
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.