RAG & Embeddings Agent Skills

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Build production-ready LLM applications, advanced RAG systems, and intelligent agents with vector search, multimodal AI, and enterprise integrations.

Index and search code repositories, documentation, research papers, HuggingFace datasets, local folders, and packages via the Nia API, with AI-powered research and code advisor capabilities.

Provides a standardized interface for ingesting raw data across domains such as genomics, network analysis, document review, and spatial mapping, converting it into semantic vectors for agent use.

Orchestrates AI/ML development workflows covering LLM applications, RAG systems, AI agents, ML pipelines, and observability.

Guides production AI systems: RAG pipelines, embeddings, vector databases, fine-tuning, prompt engineering, cost estimation, quality evals, and caching.

Store and query embeddings with metadata, vector and full-text search, and filtering. Integrates with LangChain and LlamaIndex for RAG and semantic search applications.

Builds a configurable scraping agent that collects data from APIs, HTML, or RSS, enriches it with Gemini AI scoring, and stores results in Notion, Google Sheets, Supabase, or local files.

Indexes and searches local Markdown notes and docs with BM25 keyword search, vector semantic search, and local LLM reranking, all running offline without API keys.

Provides guidance on using the ZVec in-process vector database for efficient similarity search and embedding storage in agent memory systems.

Index local folders and search documents with BM25, vector, or hybrid queries, plus AI answers with citations and a web UI.