AI Agent Hub
Back to plugins
🤖

dsh-database-design

Model Inference Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install satan9394/dsh-database-design

Paste the following prompt into your AI chat to install this plugin:

To install this plugin in DeepSeek Harness, visit https://github.com/satan9394/dsh-database-design for details and run dsh plugin install satan9394/dsh-database-design in your terminal.

About this plugin

dsh-database-design is a DeepSeek Harness plugin that brings database-design assistance into model-inference workflows. When developers spin up inference services, they frequently need to plan table schemas, field naming conventions, and indexing strategies alongside their model calls. This plugin slots that design step directly into the Harness pipeline, turning structured data-layer thinking into a reusable, traceable workflow stage.

Published under the MIT licence, the project is lightweight and carries minimal dependencies, so it can be inserted as a discrete stage in an existing inference pipeline. For developers who want to keep data-layer planning and model invocation in the same toolchain, it offers a low-friction entry point that prevents design notes from being scattered across ad-hoc documents that fall out of sync with the actual flow.

If you are building model-inference services on DeepSeek Harness and want a dedicated database-design helper inside that pipeline, dsh-database-design is worth evaluating. It is well suited to solo developers and small teams who need a concise, plug-and-play companion in their broader data-architecture toolkit.

Use Cases

  • When planning table schemas and field definitions for inference services in a DeepSeek Harness workflow
  • When embedding the database-design step as a discrete stage in a model-inference pipeline
  • When defining indexing strategies and naming conventions for the data layer of an inference service

Best For

  • Developers building inference services on DeepSeek Harness
  • Engineers who want data-layer planning and model invocation in the same toolchain
  • Solo developers and small teams seeking a lightweight, plug-and-play workflow companion