Best practices for building on-device AI features in React Native using React Native ExecuTorch. Use when the user wants to add AI to a mobile app without cloud dependencies: chatbots and assistants, image classification, object detection, OCR and document parsing, style transfer, image generation, speech-to-text, text-to-speech, voice activity detection, semantic search with embeddings, real-time camera AI with VisionCamera, or vision-language image understanding. Also use when the user mentions offline AI, on-device ML, privacy-preserving AI, reducing cloud API costs or latency, running models locally on mobile, or downloading and managing ML models. Covers react-native-executorch hooks (useLLM, useClassification, useObjectDetection, useOCR, useSemanticSegmentation, useInstanceSegmentation, useStyleTransfer, useTextToImage, useImageEmbeddings, useSpeechToText, useTextToSpeech, useVAD, useTextEmbeddings, useExecutorchModule), tool calling, structured output, VLMs, model loading, and resource management.
react-native-best-practices
rodrgds/ark
Software Mansion's best practices for production React Native and Expo apps on the New Architecture. MUST USE before writing, reviewing, or debugging ANY code in a React Native or Expo project. If the working directory contains a package.json with react-native, expo, or expo-router as a dependency, this skill applies. Trigger on: any code task in a React Native/Expo project, 'React Native', 'Expo', 'New Architecture', 'Reanimated', 'Gesture Handler', 'react-native-svg', 'ExecuTorch', 'react-native-audio-api', 'react-native-enriched', 'Worklet', 'Fabric', 'TurboModule', 'WebGPU', 'react-native-wgpu', 'TypeGPU', 'GPU shader', 'WGSL', 'svg', 'animation', 'gesture', 'audio', 'rich text', 'AI model', 'multithreading', 'chart', 'vector', 'image filter', 'shared value', 'useSharedValue', 'runOnJS', 'scheduleOnRN', 'thread', 'worklet', or any question involving UI, graphics, native modules, or React Native threading and animation behavior. Also use when a more specific sub-skill matches.
Gathers design context for a project. Runs a multi-round discovery interview when context is missing and writes PRODUCT.md (strategic: users, brand, principles) and, when code exists to analyze, DESIGN.md (visual: colors, typography, components). Every other command reads these files before doing work. Use once per project.
sqlite-vec extension for vector similarity search in SQLite. Use when storing embeddings, performing KNN queries, or building semantic search features. Triggers on sqlite-vec, vec0, MATCH, vec_distance, partition key, float[N], int8[N], bit[N], serialize_float32, serialize_int8, vec_f32, vec_int8, vec_bit, vec_normalize, vec_quantize_binary, distance_metric, metadata columns, auxiliary columns.