Loading...
Loading...
Found 146 Skills
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
Learn how to enhance your CMS like PocketBase with AI-powered content recommendations using text embeddings, SQLite, and k-nearest neighbor search for efficient and scalable related content suggestions.
OpenAI API via curl. Use this skill for GPT chat completions, DALL-E image generation, Whisper audio transcription, embeddings, and text-to-speech.
Analyze AI/ML technical content (papers, articles, blog posts) and extract actionable insights filtered through enterprise AI engineering lens. Use when user provides URL/document for AI/ML content analysis, asks to "review this paper", or mentions technical content in domains like RAG, embeddings, fine-tuning, prompt engineering, LLM deployment.
Go context.Context usage patterns including parameter placement, avoiding struct embedding, and proper propagation. Use when working with context.Context in Go code for cancellation, deadlines, and request-scoped values.
AWS Bedrock foundation models for generative AI. Use when invoking foundation models, building AI applications, creating embeddings, configuring model access, or implementing RAG patterns.
Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when: building RAG, vector search, embeddings, semantic search, document retrieval.
Build on-device AI into React Native apps using ExecuTorch. Provides hooks for LLMs, computer vision, OCR, audio processing, and embeddings without cloud dependencies. Use when building AI features into mobile apps - AI chatbots, image recognition, speech processing, or text search.
Use when you need SVG diagram rules, layout patterns, or embedding guidance for slide decks and want the minimal SVG-focused reading path.
プロダクトデモ動画を自動生成。百聞は一見にしかず、を体現。Use when user mentions '/generate-video', video generation, product demos, or visual documentation. Do NOT load for: embedding video players, live demos, video playback features. Requires Remotion setup.
Guides development with SAP AI Core and SAP AI Launchpad for enterprise AI/ML workloads on SAP BTP. Use when: deploying generative AI models (GPT, Claude, Gemini, Llama), building orchestration workflows with templating/filtering/grounding, implementing RAG with vector databases, managing ML training pipelines with Argo Workflows, configuring content filtering and data masking for PII protection, using the Generative AI Hub for prompt experimentation, or integrating AI capabilities into SAP applications. Covers service plans (Free/Standard/Extended), model providers (Azure OpenAI, AWS Bedrock, GCP Vertex AI, Mistral, IBM), orchestration modules, embeddings, tool calling, and structured outputs.
Skill for operating PocketBase backend via REST API and Go package mode. Provides collection CRUD, record CRUD, superuser/user authentication, backup & restore, migration file generation (JS and Go), Go hooks, custom routes, and design guidance for API rules, relations, and security patterns. Use for requests related to PocketBase, pb_migrations, collection management, record operations, Go framework embedding, and backend design.