gguf-quantization
davila7GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
quantizing-models-bitsandbytes
davila7Quantizes LLMs to 8-bit or 4-bit for 50-75% memory reduction with minimal accuracy loss. Use when GPU memory is limited, need to fit larger models, or want faster inference. Supports INT8, NF4, FP4 formats, QLoRA training, and 8-bit optimizers. Works with HuggingFace Transformers.
llama-cpp
davila7Runs 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.
llama-factory
davila7Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support
cryptowallet
openclawComplete cryptocurrency wallet management for Web3, DeFi, and blockchain applications. Create and manage EVM (Ethereum, Polygon, BSC, Arbitrum, Optimism, Base, Avalanche) and Solana wallets with encrypted local storage. Query balances for native tokens (ETH, MATIC, BNB, SOL) and standard tokens (ERC20, SPL). Send transactions, interact with smart contracts, and manage multiple addresses across 12+ networks. Secure password-protected key storage with AES-256 encryption. Use for: (1) Creating new crypto wallets, (2) Importing existing wallets, (3) Checking token balances across chains, (4) Sending cryptocurrency and tokens, (5) Interacting with DeFi protocols and smart contracts, (6) Multi-chain portfolio management, (7) NFT transfers, (8) Blockchain development and testing. Keywords: crypto, cryptocurrency, wallet, blockchain, ethereum, solana, web3, defi, token, erc20, nft, smart contract, metamask alternative, hardware wallet, cold storage, hot wallet, blockchain wallet, digital wallet, bitcoin.
registry-component-patterns
TheOrcDevRegister components in registry.json for shadcn/ui add command. Apply when adding new 8-bit components to the component library.
shadcn-ui-conventions
TheOrcDevUI component conventions for 8-bit styled components. Use when working with shadcn/ui components or implementing retro-styled UI elements.
component-wrapper-architecture
TheOrcDevBest practices for wrapping shadcn/ui components. Apply when creating 8-bit styled variants of existing shadcn/ui components.
fumadocs-component-docs
TheOrcDevCreate component documentation with installation, usage examples, and preview sections. Apply when documenting 8-bit components with proper structure and examples.
8-bit-pixel-art-patterns
TheOrcDevCreate pixelated borders, shadows, and effects for 8-bit retro components. Apply when building 8-bit styled UI components that need authentic pixel art aesthetics.
rendering-animate-svg
TheOrcDevWrap animated SVG elements in a div to enable hardware acceleration. Apply when animating SVG icons or elements, especially in 8-bit retro components with pixel art animations.
animation-performance-retro
TheOrcDevOptimize 8-bit animations for smooth performance. Apply when creating animated pixel art, game UI effects, or any retro-styled animations.
8bit-docs-patterns
TheOrcDevCreate documentation with gaming-specific examples, retro styling, and 8-bit terminology. Apply when documenting gaming blocks, RPG components, or retro-styled UI elements.
retro-css-architecture
TheOrcDevOrganize 8-bit CSS with custom properties, pixel fonts, and responsive pixel art. Apply when creating or modifying retro-styled components and their CSS.
fumadocs-registry-integration
TheOrcDevRegister 8-bit components in registry.json for shadcn/ui add command. Apply when adding new components to the component library distribution.
wendy-contributing
JoannisExpert guidance on contributing to WendyOS: Yocto builds, agent internals, E2E testing, and system architecture. Use when developers mention: (1) building WendyOS images, (2) meta-wendyos layers or bitbake, (3) wendy-agent development or internals, (4) containerd or nerdctl on WendyOS, (5) E2E tests for wendy-agent, (6) Yocto recipes or bbappend files, (7) mDNS/Avahi service configuration, (8) device identity or UUID generation.
win31-audio-design
curiositechExpert in Windows 3.1 era sound vocabulary for modern web/mobile apps. Creates satisfying retro UI sounds using CC-licensed 8-bit audio, Web Audio API, and haptic coordination. Activate on 'win31 sounds', 'retro audio', '90s sound effects', 'chimes', 'tada', 'ding', 'satisfying UI sounds'. NOT for modern flat UI sounds, voice synthesis, or music composition.
narrative-text-visualization
antvisGenerate structured narrative text visualizations from data using T8 Syntax. Use when users want to create data interpretation reports, summaries, or structured articles with semantic entity annotations. T8 is designed for unstructured data visualization where T stands for Text and 8 represents a byte of 8 bits, symbolizing deep insights beneath the text.
pixel-art-sprites
omer-metinSpecialist in pixel art creation, sprite animation, and limited palette designUse when "pixel art, sprites, sprite sheet, sprite animation, retro game art, 8-bit, 16-bit, character sprites, tile art, pixel animation, aseprite, pixel-art, sprites, animation, game-art, retro, indie-games, 2d-graphics, sprite-sheets" mentioned.
pixel-art
omer-metinDeep pixel art expertise covering fundamentals, limited palettes, dithering patterns, subpixel animation, tile design, retro hardware constraints, and HD-2D hybrid techniques. Knowledge Claude wouldn't normally have from indie masters like Pedro Medeiros (saint11). Use when "pixel art, sprite art, dithering, limited palette, subpixel animation, tileset, autotile, pillow shading, selective outline, selout, hue shifting, color ramp, NES style, SNES style, HD-2D, Aseprite, retro game art, 8-bit, 16-bit, pixel-art, sprites, animation, retro, indie-games, dithering, palettes, tiles, subpixel, aseprite, NES, SNES, GBA, HD-2D" mentioned.
model-quantization
Expert capability for AI model quantization and optimization. Covers 4-bit and 8-bit quantization, GGUF conversion, memory optimization, and quality–performance tradeoffs for deploying large language models in resource-constrained JARVIS environments.
llama-cpp
AXGZ21Runs 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.