Preface

DeepSeek Harness (DSH) adopts a plugin-based architecture, allowing developers to extend capabilities on demand. When building agents that involve domain-specific data (such as quantum computing), connecting directly to data sources often requires additional configuration. The dsh-future-quantum-computing plugin provides an interface for accessing quantum computing entries and their details, and belongs to model inference tools.

Plugin Introduction

The plugin is maintained by uckkk. It is implemented in pure Node.js code and has no network dependencies at runtime. After installation, the plugin registers the corresponding tools in the current session, allowing developers to call the data without writing extra code.

Installation and Dependencies

Before installing the plugin, ensure that the environment meets the following dependencies:

Installation Command

dsh plugin add github:uckkk/dsh-future-quantum-computing

Dependency Requirements
* @deepseek-ai/cordis (^4.0.1)
* @deepseek-ai/dsh-tools (>=0.1.0-rc.6)

Core Features

The plugin provides the following two tools:

  1. list_qc: Lists quantum computing entries.
  2. get_qc: Queries quantum computing entry details by id or name.

Usage

After successful installation, you can directly call the tools registered by the plugin in the session. For example, you can first call list_qc to get the list, and then use get_qc to retrieve a specific entry by name or ID.

Notes

  • The plugin is written entirely in Node.js and does not rely on external network requests.
  • Because the plugin runs with the permissions of the current process, it is recommended to review the source code before use.
  • The project is licensed under the MIT License.

For more technical details, refer to GitHub or the DSH Directory.