Performance
Performance profiling and optimization
symfony:doctrine-transactions
by MakFly
Evolve Symfony Doctrine models and schema safely with integrity, performance, and rollout discipline. Use for doctrine transactions tasks.
symfony:doctrine-fixtures-foundry
by MakFly
Evolve Symfony Doctrine models and schema safely with integrity, performance, and rollout discipline. Use for doctrine fixtures foundry tasks.
voice-apply
by jmagly
Applies a voice profile to transform content. Use when user asks to write in a specific voice, match a tone, apply a style, or transform content to sound like a particular voice profile.
performance-digest
by jmagly
Benchmark data: .aiwg/marketing/benchmarks/
dma-attack-techniques
by gmh5225
Guide for Direct Memory Access (DMA) attack techniques using FPGA hardware. Use this skill when researching PCIe DMA attacks, pcileech, FPGA firmware development, or hardware-based memory access for game security research.
ReasoningBank with AgentDB
by ruvnet
"Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay systems."
extreme-software-optimization
by pedronauck
Profile-driven performance optimization with behavior proofs. Use when: optimize, slow, bottleneck, hotspot, profile, p95, latency, throughput, or algorithmic improvements.
vercel-react-best-practices
by lobehub
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.
Hooks Automation
by ruvnet
Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development workflows.
optimizing-attention-flash
by NousResearch
Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.
llama-cpp
by NousResearch
Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.
agent-orchestration-improve-agent
by sickn33
"Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration."
V3 Memory Unification
by ruvnet
"Unify 6+ memory systems into AgentDB with HNSW indexing for 150x-12,500x search improvements. Implements ADR-006 (Unified Memory Service) and ADR-009 (Hybrid Memory Backend)."
trackpy-particle-tracking
by jaechang-hits
"Python library for tracking particles (fluorescent spots, colloids, vesicles, cells) in video microscopy using the Crocker-Grier algorithm. Core modules: locate particles in single frames, batch-process image sequences, link positions into trajectories, filter short-lived tracks, and compute mean squared displacement (MSD) for diffusion analysis. Supports 2D and 3D tracking with subpixel accuracy. Integrates with pims for reading TIF stacks, AVI, and image series. Use when you need quantitative single-particle tracking (SPT) from fluorescence or brightfield video and downstream diffusion coefficient extraction."
threejs-postprocessing
by CloudAI-X
Three.js post-processing - EffectComposer, bloom, DOF, screen effects. Use when adding visual effects, color grading, blur, glow, or creating custom screen-space shaders.
apollo-performance-tuning
by Dicklesworthstone
Optimize Apollo.io API performance. Use when improving API response times, reducing latency, or optimizing bulk operations. Trigger with phrases like "apollo performance", "optimize apollo", "apollo slow", "apollo latency", "speed up apollo".
threejs-interaction
by CloudAI-X
Three.js interaction - raycasting, controls, mouse/touch input, object selection. Use when handling user input, implementing click detection, adding camera controls, or creating interactive 3D experiences.
cobrapy
by K-Dense-AI
Constraint-based metabolic modeling (COBRA). FBA, FVA, gene knockouts, flux sampling, SBML models, for systems biology and metabolic engineering analysis.
backlink-analyzer
by aaron-he-zhu
'Use when the user asks to "analyze backlinks", "check link profile", "find toxic links", "link building opportunities", "off-page SEO", "who links to me", "I have spammy links", "how do I get more backlinks", or "disavow links". Analyzes backlink profiles to understand link authority, identify toxic links, discover link building opportunities, and monitor competitor link acquisition. Essential for off-page SEO strategy. For internal link analysis, see internal-linking-optimizer. For competitor link profiles, see competitor-analysis.'
android-jetpack-compose-expert
by sickn33
Expert guidance for building modern Android UIs with Jetpack Compose, covering state management, navigation, performance, and Material Design 3.
peft-fine-tuning
by NousResearch
Parameter-efficient fine-tuning for LLMs using LoRA, QLoRA, and 25+ methods. Use when fine-tuning large models (7B-70B) with limited GPU memory, when you need to train <1% of parameters with minimal accuracy loss, or for multi-adapter serving. HuggingFace's official library integrated with transformers ecosystem.
dask
by K-Dense-AI
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
get-available-resources
by K-Dense-AI
This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
cirq
by K-Dense-AI
Google quantum computing framework. Use when targeting Google Quantum AI hardware, designing noise-aware circuits, or running quantum characterization experiments. Best for Google hardware, noise modeling, and low-level circuit design. For IBM hardware use qiskit; for quantum ML with autodiff use pennylane; for physics simulations use qutip.