by notch-ai · MCP Server · ★ 61
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
AutoSteer -lightgrey) AutoSteer is a desktop application that enhances your Claude Code experience with multi-workspace management. Built with Electron, it works across macOS, Linux, and Windows (via WSL), allowing you to manage multiple isolated workspaces with persistent sessions and seamless context switching. Demo https://github.com/user-attachments/assets/9c86ef28-7167-41c9-b4e6-7d50ba586ca2 [!NOTE] This project is not affiliated with, endorsed by, or sponsored by Anthropic. Claude is a trademark of Anthropic, PBC. This is an independent project using Claude. Features Worktree-First Architecture - Organize projects in isolated workspaces with independent file systems and contexts Persistent Sessions - Save and resume conversations per worktree, maintaining full context across work sessions Multi-Project Management - Switch seamlessly between different projects without losing state or context Per-Project Tab Management - Session tabs scoped to projects with auto-select behavior and persistent state Cross-Platform - Native
| Stars | 61 |
| Forks | 10 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 32.25/100 |
| Last Updated | 2025-11-26 |
| Created | 2025-10-21 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~1610k |
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autosteer is Desktop app for multi-workspace Claude Code management. It is categorized as a MCP Server with 61 GitHub stars.
autosteer is primarily written in TypeScript. It covers topics such as anthropic, anthropic-claude, claude.
You can find installation instructions and usage details in the autosteer GitHub repository at github.com/notch-ai/autosteer. The project has 61 stars and 10 forks, indicating an active community.
autosteer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to autosteer on Agent Skills Hub include claude-emporium, claude-historian-mcp, awesome-claude-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.