Results for “index-mapping”

SQLite-backed index over all entity files in the project. The read-side foundation for every other MCP server. Use whenever an agent needs to look up entities by ID, kind, state, or text — instead of grepping the filesystem.

Analyze codebase structure, generate domain-specific sub-skills (UI, Backend, Database, etc.), and create agent-guidance files to help AI agents navigate and develop consistently within a project. Use when onboarding to a new codebase, creating project documentation, or setting up agent guidance systems.

Manages vector embeddings with Pinecone for semantic search, recommendation, and RAG pipelines.

Indexes Megatron Bridge recipes and recommends the best starting config based on model, GPU count, and training goal.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.

Extracts structured business data from Google Maps search results using the BrowserAct API. Provide search keywords, language, and country filters to get clean, usable business data.

Reduce high-dimensional data with UMAP for visualization, clustering preprocessing, and supervised or semi-supervised learning, including parameter tuning guidance.

Expert en cartes et géolocalisation avancé (OpenStreetMap, Nominatim, OSRM, geocoding, routing, timezones, GIS)