Introduction

In the DeepSeek Harness (DSH) plugin ecosystem, building tools for specific scenarios usually requires writing standalone logic modules. For the cycling-training scenario, directly calling external APIs or relying on network conditions may introduce latency or instability. The dsh-ride-train plugin provides a localized solution, focusing on generating cycling exercise plans.

Plugin Overview

This plugin is maintained by uckkk, and its core function is to provide cycling-training plans. It is fully implemented in Node.js and does not depend on network requests during runtime, making it suitable for offline or low-latency scenarios.

Core Features

  • Cycling training plan generation: After receiving parameters, it returns the corresponding cycling exercise plan.
  • Pure Node.js implementation: The logic runs locally and does not involve external HTTP requests.

Installation and Dependencies

Before installing, make sure your current environment satisfies the following dependency requirements:
* The version of @deepseek-ai/cordis must be ^4.0.1 or higher
* The version of @deepseek-ai/dsh-tools must be at least >=0.1.0-rc.6

Run the following command to install it:

dsh plugin add github:uckkk/dsh-ride-train

Usage

After installation, you can call the tools registered by this plugin in a DSH session. The specific invocation method depends on the current session’s context configuration, and the tools are automatically mounted to the plugin instance.

Notes

  • Runtime environment: The plugin runs with the permissions of the current DSH process. Please make sure you fully understand the source code and license (MIT) before installing it.
  • Version compatibility: The dependency version constraints are relatively strict. Please ensure that the versions in your local environment match the requirements.

Summary

dsh-ride-train provides a lightweight cycling-training plan generation tool for the DSH plugin ecosystem. By replacing network requests with local computation, it reduces external runtime dependencies. For more details, please refer to the GitHub repository or the SkillHub directory.