Shopify Functions allow developers to customize the backend logic that powers parts of Shopify. Available APIs: Discount, Cart and Checkout Validation, Cart Transform, Pickup Point Delivery Option Generator, Delivery Customization, Fulfillment Constraints, Local Pickup Delivery Option Generator, Order Routing Location Rule, Payment Customization
Hydrogen storefront implementation cookbooks. Some of the available recipes are: B2B Commerce, Bundles, Combined Listings, Custom Cart Method, Dynamic Content with Metaobjects, Express Server, Google Tag Manager Integration, Infinite Scroll, Legacy Customer Account Flow, Markets, Partytown + Google Tag Manager, Subscriptions, Third-party API Queries and Caching. MANDATORY: Use this API for ANY Hydrogen storefront question - do NOT use Storefront GraphQL when 'Hydrogen' is mentioned.
MUST be used first when prompts mention Metafields or Metaobjects. Use Metafields and Metaobjects to model and store custom data for your app. Metafields extend built-in Shopify data types like products or customers, Metaobjects are custom data types that can be used to store bespoke data structures. Metafield and Metaobject definitions provide a schema and configuration for values to follow.
Run a validated Admin GraphQL operation against a specific store using Shopify CLI. Use this when the user wants an executable store workflow, not just the query or mutation text. If the answer should include `shopify store auth` and `shopify store execute`, choose this API. Choose this for 'my store', 'this store', a store domain, product reads on a merchant store, low-inventory lookups, product updates, and warehouse/location inventory changes. Examples: 'Show me the first 10 products on my store', 'Find products with low inventory on my store', 'Set inventory at the Toronto warehouse so SKU ABC-123 is 12'.
Software Mansion's best practices for audio in React Native using react-native-audio-api. Covers audio playback (buffer sources, oscillators, streaming), recording (file, callback, graph), audio effects (gain, filters, delay, convolver, panner, waveshaper), real-time analysis and visualization, audio worklets (custom synthesis, UIRuntime/AudioRuntime), system integration (sessions, interruptions, permissions), and testing. Trigger on: react-native-audio-api, AudioContext, AudioRecorder, AudioBuffer, AudioBufferSourceNode, AudioBufferQueueSourceNode, OscillatorNode, StreamerNode, GainNode, BiquadFilterNode, DelayNode, ConvolverNode, StereoPannerNode, WaveShaperNode, AnalyserNode, AudioParam, AudioManager, PlaybackNotificationManager, RecordingNotificationManager, WorkletNode, WorkletSourceNode, WorkletProcessingNode, audio playback, record audio, microphone, waveform, audio visualization, audio streaming, audio worklet, or any React Native feature that captures, processes, or emits sound.
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: AI chatbots and assistants, image classification, object detection, text recognition and document parsing (OCR), style transfer, image generation, speech-to-text transcription, text-to-speech synthesis, 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
Assess application readiness for cloud-native deployment. Evaluate against 12-factor app principles, containerization readiness, and Azure service fit. Adaptive depth.
Diagnose and resolve Azure deployment failures by analyzing error output, checking Azure resource state, and suggesting fixes. Use when azd provision fails, azd deploy fails, smoke tests fail against live deployment, container images fail to push/pull, or any Phase 6 deployment error occurs. Trigger on deployment errors, infrastructure failures, or post-deploy smoke test failures.
Provision Azure infrastructure, deploy to Azure Container Apps, and verify via smoke tests. Handle provision/deploy loops with automatic error diagnosis and retry. Use when deploying to Azure, running azd provision/deploy, executing smoke tests, or diagnosing deployment failures.
Plan the journey to cloud-native deployment on Azure. Generate increments for containerization, configuration externalization, infrastructure-as-code, observability, CI/CD, and Azure service provisioning. Each increment feeds into the standard Phase 2 delivery pipeline.
Researches archives, contemporary accounts, and timeline reconstruction. Use when the album subject involves historical events that need primary source verification.
Researches personal backgrounds, interviews, motivations, and humanizing details. Use when research needs biographical context about people involved in the album's subject.
Combine multiple insights into coherent narrative
Extract Eugene's perspective on a topic (called by Ruby or user)
Comprehensive testing playbook for Local Brain Search memory improvements (Phases 1, 3, 4)
Captures human source verification for tracks, timestamps it, and updates track files. Use when sources need human review before generation.
Use when building CI/CD pipelines, containerizing applications, managing Kubernetes clusters, provisioning cloud infrastructure with Terraform, implementing deployment strategies (blue-green, canary, rolling), setting up monitoring/observability, optimizing cloud costs, or handling infrastructure incident response.
