Rendiment
Anàlisi i optimització del rendiment
memory-forget
VeriTeknik
"Delete specific memories or clear memory categories. Use with caution - forgotten memories cannot be recovered."
memory-status
VeriTeknik
"Show memory system status including active session, ring counts, and recent observations"
platform-tools
VeriTeknik
"Complete reference for all Plugged.in MCP tools - memory, knowledge base, clipboard, documents, and notifications. Use when you need to know what tools are available."
latency-critical-systems
SeuPut0705
Optimize and verify latency-sensitive systems — realtime dashboards, market data feeds, streaming agents, execution gateways, queues, and caches — by tracking p50/p95/p99 latency, freshness age, and queue depth, mapping hot paths, and running live readbacks. Use when p95 latency, throughput, or data freshness matters.
domain-memory-extract
ArthurC02
Extract DDD information into the Domain Registry from a requirement, from existing code, or from a requirement change or refactoring, recording only the domain facts and their evidence.
knowledge
JinFanZheng
External knowledge capture and retrieval. Trigger when user wants to save notes, bookmarks, code snippets, or reference material for later access.
memory
JinFanZheng
Persistent memory for cross-session personalization. Trigger when user shares identity, preferences, relationships, or facts worth remembering.
godot-optimization
Zate
Expert knowledge of Godot performance optimization, profiling, bottleneck identification, and optimization techniques. Use when helping improve game performance or analyzing performance issues.
database-engineer
JochenYang
Database architecture design, schema optimization, query performance tuning, and data migration. Handles PostgreSQL, MySQL, MongoDB, Redis with focus on scalability, indexing strategies, and transaction management.
debate-workflow
rysweet
Structured multi-perspective debate for important architectural decisions and complex trade-offs
model-evaluation-benchmark
rysweet
Automated reproduction of comprehensive model evaluation benchmarks following the Benchmark Suite V3. Auto-activates for model benchmarking, comparison evaluation, or performance testing between AI models.
engineer-analyst
rysweet
Analyzes technical systems and problems through engineering lens using first principles, systems thinking, design methodologies, and optimization frameworks. Provides insights on feasibility, performance, reliability, scalability, and trade-offs. Use when: System design, technical feasibility, optimization, failure analysis, performance issues. Evaluates: Requirements, constraints, trade-offs, efficiency, robustness, maintainability.
n-version-workflow
rysweet
N-version programming for critical implementations - generates N independent solutions and selects the best through comparison
dask
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
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
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
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
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
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
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
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
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
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
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.