In the DeepSeek Harness (DSH) plugin ecosystem, tools tailored to specific domain scenarios are often more efficient than general-purpose agents. If you need a method that requires no network dependency and directly generates running interval training plans through local computation, the dsh-interval plugin provides a lightweight solution.

What This Is

dsh-interval is a DSH plugin maintained by istone. It returns running training plans based on parameters through local computation logic. The plugin does not rely on external network requests, making it suitable for scenarios that require quickly generating fixed-format training plans.

Core Features

  • Returns running training plans based on parameters: Generates a training plan dynamically based on the input parameters.
  • interval tool: The core tool registered by the plugin, used to perform the functionality above.

Installation and Activation

To install the plugin, run the following command in the terminal:

dsh plugin add github:uckkk/dsh-interval

After installation is complete, the plugin automatically registers its tools into the current DSH session.

Typical Usage

After installing the plugin and loading it in a session, simply invoke the tool registered by the plugin in the conversation. The exact invocation method depends on the specific DSH configuration, but usually referring directly to the interval tool can trigger the corresponding computation logic.

Applicable Scenarios and Notes

  • Applicable scenarios: Suitable for scenarios that require locally generating running interval training plans and want the computation process to avoid network requests.
  • Notes:
    1. The plugin is implemented entirely in Node and has no network dependencies.
    2. Before installation, ensure that @deepseek-ai/cordis (version ^4.0.1) and @deepseek-ai/dsh-tools (version >=0.1.0-rc.6) are installed as peerDependencies.

Summary

This plugin provides a lightweight local training plan generation tool for the DSH ecosystem, making it suitable for scenarios with specific computational needs. For more details or feedback, visit the plugin directory or view the source code.