Complete Tutorial: Installing MCP Connector in WorkBuddy and Syncing Skills Across AI Workstations

The Pain Point of Sharing Skills Across Multiple AI Workstations

Anyone who uses multiple AI workstations has almost certainly hit the same wall: trying to install the same Skill on two coding tools means either manually copying folders or repeatedly pasting MCP (local connector) configurations — and after half an hour of fiddling, synchronization still might not work.

This inefficiency fundamentally stems from the fact that different AI workstations (such as Cursor and WorkBuddy) each maintain independent skill libraries and configuration files, lacking a unified management and synchronization mechanism. AI-native development tools are currently in a period of rapid growth. Beyond Cursor and WorkBuddy, the market includes similar products like Windsurf, Trae, and Augment Code. All of these tools deeply embed large language models into the development workflow, but each maintains its own independent configuration system and plugin ecosystem. Developers often need to switch between multiple AI workstations depending on the task at hand, making fragmented skill management a significant pain point. For developers who value efficiency, hunting through different AI workstations to find, copy, and paste skill files is an enormous waste of time.

In the context of AI workstations, a Skill typically refers to a set of predefined prompt templates, tool invocation rules, or workflow configurations stored as files in specific directories on local disk. For example, Cursor's Rules files are stored in the .cursor/rules/ folder under the project root, while global configurations reside in the user directory. Different workstations have their own standards for skill file formats and storage paths, which creates difficulties for cross-platform migration. Traditional synchronization methods include manually copying files, using symbolic links (symlinks), or managing through Git repositories — but these approaches are either error-prone or require a certain level of system administration knowledge.

This article walks through the complete process from installing the local connector (MCP) in WorkBuddy to automatically syncing a single set of skills across all coding tools — entirely through AI conversation, with no manual file moving required.

Installing the Local MCP Connector in WorkBuddy

The starting point of the entire workflow is having WorkBuddy automatically configure the local connector. The MCP mentioned here stands for Model Context Protocol, an open standard protocol released by Anthropic in late 2024. Its core design philosophy is to provide large language models with a standardized way to connect to external data sources and tools, similar to how USB-C unified charging and data transfer standards across devices. In the MCP architecture, the AI workstation acts as the "client" while local or remote services act as "servers," communicating through a unified JSON-RPC protocol. This means developers only need to write a tool interface once and can reuse it across any AI application that supports MCP, without having to adapt separately for each platform.

Once configuration is complete, the system will prompt you to trust and enable the local MCP service — simply follow the instructions.

Then switch back to the conversation, tell the AI that activation is complete, and ask it to confirm the MCP's built-in capabilities. There's a critical detail to note here: some connector features may require restarting WorkBuddy and starting a new conversation to take effect.

After restarting and starting a new conversation, you can see that all connector features work properly and many skills have already been loaded. At this point, a "migration feature" will appear in the tool list — this is the core capability that enables cross-workstation synchronization later on.

Migrating Skills from Cursor to WorkBuddy

Once configuration is complete, you can try migrating Cursor's skills over for use. One commendable detail is that WorkBuddy includes a safety reminder mechanism when executing commands, effectively preventing accidental operations.

For example, during the migration process, a key reminder appears on screen, clearly stating that this is a "move" operation rather than a "copy." There's a deeper rationale behind this design choice — in software engineering, the "Single Source of Truth" principle is the golden rule for avoiding state inconsistency. If a copy strategy were used, the same skill file would produce duplicates in multiple locations, and when a user modifies one copy, the others wouldn't automatically update, eventually leading to version fragmentation. The move strategy ensures that skill files always have only one physical storage location (uniformly hosted by the MCP server), with each AI workstation reading from the same data source through the MCP protocol. While this design requires an extra "refresh" step during migration, from a long-term maintenance perspective, it eliminates the risk of data inconsistency and represents a more robust architectural choice.

The author mentions that this migration tool is self-developed, so it can be used directly to "host" skills — eliminating the need to laboriously search through and copy piles of skill files across various AI workstations. The entire thinking and execution process can be completed through AI conversation, saving a tremendous amount of manual effort.

After successful migration, the tool thoughtfully reminds you that the skills on the Cursor side are "gone" (because it was a move, not a copy) and that Cursor needs to be notified to refresh. At this point, simply switch to Cursor and have it read the latest skills — Cursor will then list all the skills that were just migrated. At this point, Cursor and WorkBuddy share the same set of skills.

Verifying Cross-Workstation Skill Synchronization

After completing the migration, you need to verify that the local connector features are all working properly. After confirming via screenshots, you can see that the configured local MCP connector works perfectly, achieving the effect of "one-click skill migration, available across all local AI workstations."

At this point, asking WorkBuddy to simply list other available tools reveals a complete skill management system. The entire process can be accomplished through AI conversation, completely eliminating the hassle of copying, pasting, and clicking around everywhere.

Compared to other open-source Skill managers, the core advantage of this approach lies in being entirely driven by AI conversation: users don't need to remember the specific location of skills or manually move files — the AI handles scheduling and management on its own. This is made possible by MCP's standardized design — the AI workstation, acting as an MCP client, can discover and invoke various tool capabilities provided by the MCP server through a unified interface, including file operations, skill retrieval, and configuration management. The entire process is completely transparent to the user.

Reverse Verification: Skills Installed in WorkBuddy Auto-Sync to Cursor

To verify bidirectional synchronization, the author also demonstrated the reverse flow: installing a few skills from WorkBuddy's skill marketplace, having WorkBuddy automatically scan and migrate them to the self-built tool host, and ultimately making them available in Cursor as well.

After migration is complete, simply re-read the latest skills in Cursor, and both workstations share the same set of skills. The entire process is still handled by the AI itself — scheduling and executing autonomously. Users only need to tell the AI "what I need to do," and it will invoke, search, and find relevant skills to complete the task.

The results on both sides are completely identical — WorkBuddy downloads skills with one click, syncs with one click, and all locally installed skills are synchronized and available across every AI workstation.

Summary and Reflections

The core value of this approach lies in transforming what used to be a tedious manual synchronization process into pure AI conversation-based management. It addresses three key pain points in multi-AI workstation scenarios:

• Complex configuration: No more manual pasting of MCP configurations;
• Scattered skills: A single set of Skills auto-syncs across multiple workstations;
• Memory burden: No need to remember skill locations — the AI automatically schedules and invokes them.

From a broader perspective, this approach reflects the trend of AI tool ecosystems moving from "siloed isolation" toward "interconnectivity." The emergence of the MCP protocol provides the technical foundation for this trend, and the unified skill hosting approach introduced in this article is a concrete implementation of this vision. As more and more AI workstations support the MCP standard, cross-platform tool sharing and skill synchronization will become increasingly seamless.

It's worth noting that the migration tool shown in the video is the author's own creation and uses a "move" rather than "copy" hosting logic — something to keep in mind when using it. After migration, the skills in the source workstation will be moved away and require manual refreshing. For developers who heavily use multiple AI coding tools simultaneously, this kind of unified skill management approach is well worth considering.