grepai▌

This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search.

This skill covers using LM Studio as the embedding provider for GrepAI, offering a user-friendly GUI for managing local models.

This skill provides a complete walkthrough to get GrepAI running and searching your code in 5 minutes.

This skill covers using GrepAI workspaces to manage multiple related projects with a unified search index.

This skill provides a complete reference for all tools available through GrepAI's MCP server.

Semantic code search by meaning rather than exact text strings. \n \n Searches code by intent and concept similarity using embeddings, returning ranked results with relevance scores (0.0–1.0) \n Requires GrepAI initialization, an active index created via grepai watch , and a running embedding provider like Ollama \n Supports natural language queries describing behavior or intent; 3–7 word phrases work best, with results limited via --limit flag \n Interprets scores: 0.90+ excellent match, 0.80–0

This skill covers using PostgreSQL with the pgvector extension as the storage backend for GrepAI.

This skill covers using OpenAI's embedding API with GrepAI for high-quality, cloud-based embeddings.

This skill covers integrating GrepAI with Cursor IDE using the Model Context Protocol (MCP).

This skill covers all methods to install GrepAI on any platform.