Loading...
Loading...
Found 312 Skills
When the user wants to add, optimize, or audit popups or modals for lead capture or offers. Also use when the user mentions "popup," "modal," "lightbox," "overlay," "exit-intent," "popup form," "modal design," "lead popup," "popup timing," or "popup triggers."
Use when connecting to a self-hosted memory backend, searching, storing, or managing memories, importing connection tokens, or troubleshooting retrieval issues. Use this skill whenever the user mentions memory search, RAG retrieval, embedding, memory storage, multimodal document upload, knowledge queries, or wants to connect to a memory service, even if they do not explicitly say "transcendence-memory".
Audit, plan, and safely optimize Shopify image alt text for product media, collection featured images, article featured images, and article inline images. Use when a merchant wants an AI agent to scan Shopify images, test whether the active AI model can inspect images, generate concise alt text with multimodal image understanding when available or context-only fallback when it is not, review the proposed changes in batches, and apply approved Shopify Admin updates.
On-device, real-time multimodal AI voice and vision assistant powered by Gemma 4 E2B and Kokoro TTS, running entirely locally via FastAPI WebSocket server.
Use this skill whenever deciding what features to extract from raw marketplace assets — listing photos, owner-entered listing metadata, sitter wizard responses — to power item-to-item (similar listings), user-to-item (homefeed ranking), or user-to-user (mutual-fit matching) recommenders in a two-sided trust marketplace. Covers asset auditing, first-principles feature decomposition from the decision the user is making, vision-feature extraction (CLIP, room-type classification, amenity detection, aesthetic and quality scoring), listing text and metadata encoding (categoricals, multi-hot amenities, H3 geo-hashing, sentence-transformer description embeddings, structured pet triples), sitter wizard design (information-gain ordering, multiple-choice over free text, genuine skippability, hard constraint versus soft preference), derived-composition patterns for i2i / u2i / u2u (precomputed ANN shelves, multi-modal fusion, two-tower affinity, symmetric mutual-fit scoring, interpretable subscores), feature quality governance (single registry, training-serving parity, coverage and drift alarms, PII scrubbing, schema versioning), and incremental value proof (one feature at a time, ablation A/B, kill reviews, exploration slice, permanent feature-free baseline). Trigger even when the user does not explicitly say "feature engineering" but is asking how to get more signal out of listing photos, listing metadata, or the sitter onboarding wizard, or how to improve i2i / u2i / u2u quality without blindly ingesting a new model.
Comprehensive psychoeducation on mental health conditions, therapy modalities, evidence-based coping techniques, psychiatric medications, and self-assessment frameworks. Educational resource only — not medical advice, diagnosis, or treatment. Use when learning about mental health concepts, understanding therapy options, exploring coping strategies, or recognizing when to seek professional help. Trigger on "mental health", "therapy types", "coping strategies", "anxiety", "depression", "ADHD", "psychiatric medication", "when should I see a therapist".
Guides agents to interactively discover customer requirements for live, bidirectional multi-agent AI systems that process continuous streams of multimodal data for real-time technical guidance and safety monitoring. Generates a custom Google Cloud solution that uses opinionated best practices and architecture guidance. Use when users need agentic assistance to design and create a multi-product solution in the cloud for live bidirectional multimodal streaming workloads.
Implements Motion (Framer Motion) animations in React applications. Covers animation presets, page transitions, modals, stagger effects, and skeleton loaders. Use when adding animations, transitions, or interactive hover effects.
Loading and using pretrained models with Hugging Face Transformers. Use when working with pretrained models from the Hub, running inference with Pipeline API, fine-tuning models with Trainer, or handling text, vision, audio, and multimodal tasks.
Upgrade any skill to v5 Hybrid format using decision theory + modal logic
Multimodal UI understanding and single-step planning via OpenAI-compatible Responses APIs. Use when you need AIQuery/AIAssert and plan-next to extract UI element coordinates, validate UI assertions, summarize screenshots, or decide the next UI action from an image. External agents handle execution via adb/hdc and multi-step loops. Defaults to Doubao models but can be pointed at other multimodal providers via base URL, API key, and model name.
Practical guidance for training MoE VLMs in Megatron Bridge. Compares FSDP and 3D-parallel approaches, using rounded lessons from Qwen3-VL, Qwen3-Next, and other multimodal experiments.