SkillMesh is a retrieval router for MCP/LLM tool catalogs. It selects top-K relevant expert cards instead of loading every tool, reducing prompt size, by improving tool selection, and lowering token cost. Supports Claude MCP, Codex skills, and OpenAI-style function schemas.
Provides context-aware skill selection for AI agents, reducing token usage by 85-98% and improving accuracy through semantic retrieval, session memory, and feedback learning.
Enables AI agents to semantically search and retrieve relevant skills from a local Markdown skill database, reducing context waste by loading only needed skills through tiered detail levels and tool calls.
A Model Context Protocol (MCP) server that provides intelligent search capabilities for discovering relevant Claude Agent Skills using vector embeddings and semantic similarity. This server implements the same progressive disclosure architecture that Anthropic describes in their Agent Skills enginee
Enables efficient code navigation and retrieval through natural language search, BM25 ranking, and fuzzy matching across multiple programming languages. It drastically reduces token usage by allowing Claude to query specific code symbols and logic instead of reading entire files.
