Knotr AI

Knotr AI focuses on one specific problem: keeping AI context and knowledge consistent across every AI tool a person or team uses. It acts as a central context layer where users define profiles, store documents in searchable knowledgebases, and package repeatable workflows as skills that can be called through the Model Context Protocol (MCP). Instead of rebuilding prompts and instructions per app, users configure them once and reuse them with compatible clients like Claude and Cursor.

What is Knotr AI? Knotr AI focuses on one specific problem: keeping AI context and knowledge consistent across every AI tool a person or team uses. It acts as a central context layer where users define profiles, store documents in searchable knowledgebases, and package repeatable workflows as skills that can be called through the Model Context Protocol (MCP). Instead of rebuilding prompts and instructions per app, users configure them once and reuse them with compatible clients like Claude and Cursor. Key Features: Profiles and Personas: Create multiple profiles that capture tone of voice, style, and constraints for different roles or projects so AI output matches the intended persona each time. Semantic Knowledgebases: Upload notes and documents into organized knowledgebases that can be searched semantically when skills run, giving AI access to richer context without manual copy paste. Reusable MCP Skills: Package prompts, parameters, and workflows as reusable skills that can be exposed over MCP and called from any compatible AI client, so the same capabilities are available across tools. Team Workspaces and Changelogs: Share skills and knowledgebases in a team workspace with built in changelogs that show who changed what and when, without needing Git repositories or pull requests for prompt updates.