Performance
Performance profiling and optimization
knowledge-fetch
by cuipengfei
知识召回技能 - 从 Project CLAUDE.md、User CLAUDE.md 和 Memory MCP 查询历史知识
auto-extract
by cuipengfei
会话学习技能 - 分析当前会话，提取学习并持久化到三层存储（项目 CLAUDE.md、用户 CLAUDE.md、Memory MCP）
foundational-principles
by cuipengfei
基础原则 - AI 助手的核心思维原则和指令框架概述。包含系统思维、辩证思维、创新思维和批判思维四大核心原则。
computer-scientist-analyst
by Zpankz
Analyzes events through computer science lens using computational complexity, algorithms, data structures, systems architecture, information theory, and software engineering principles to evaluate feasibility, scalability, security. Provides insights on algorithmic efficiency, system design, computational limits, data management, and technical trade-offs. Use when: Technology evaluation, system architecture, algorithm design, scalability analysis, security assessment. Evaluates: Computational complexity, algorithmic efficiency, system architecture, scalability, data integrity, security.
proactive-agent
by wangyendt
"让 AI 助手从「等待任务」变成「主动预见」的架构设计。包含 WAL 协议、Working Buffer、自动定时任务等经过实战验证的模式。"
agent-evaluation
by Zpankz
Evaluate and improve Claude Code commands, skills, and agents. Use when testing prompt effectiveness, validating context engineering choices, or measuring improvement quality.
alex-hormozi-pitch
by Zpankz
Create irresistible offers and pitches using Alex Hormozi's methodology from $100M Offers. Guides through value equation, guarantee frameworks, pricing psychology, and creating offers "too good not to take" for any product or service.
lean-startup
by luisschmitzheadline
Provides startup advice using Eric Ries' Lean Startup methodology focusing on Build-Measure-Learn cycles, validated learning, and rapid experimentation. Use when advising on MVPs, product iterations, pivot decisions, growth metrics, or when user mentions Lean Startup, Eric Ries, validated learning, or rapid experimentation.
java-performance
by pluginagentmarketplace
JVM performance tuning - GC optimization, profiling, memory analysis, benchmarking
react-best-practices
by ttmouse
React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React/Next.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.
telos
by ttmouse
个人认知框架系统。当涉及目标规划、决策建议、项目方向、工作方式优化时触发，或用户显式调用"/telos"。加载完整画像提供个性化响应，支持画像进化和定期复盘。
requirements-engineering
by vinnie357
Activate when creating Product Requirements Documents (PRDs) with business objectives, functional requirements, success criteria, and implementation planning
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.
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.
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.
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.