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dsh-sphere

Model Inference Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-sphere

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

Install it in DeepSeek Harness by running dsh plugin install uckkk/dsh-sphere; the source code is available at https://github.com/uckkk/dsh-sphere.

About this plugin

dsh-sphere tackles a narrow but practical problem: when a model-inference session needs sphere geometry computations, you are often forced to spin up an external service, configure network endpoints, or pull in a heavy dependency. dsh-sphere consolidates those calculations into a pure Node.js plugin with zero network calls and no extra processes, making sphere math an in-session, ready-to-use tool.

On the capability side, the plugin is implemented entirely in Node with no network dependency whatsoever. After installation it automatically registers a set of callable tools with the current session. Developers do not need to configure endpoints, handle authentication, or manage environment variables; they simply invoke the tool within the conversation and receive the result. The path from intent to answer is intentionally short, which makes it easy to slot into an existing inference pipeline as a geometry operator.

It is a good fit for local offline development, air-gapped CI pipelines, interactive teaching demos that verify sphere formulas on the fly, and any sandboxed inference setup that demands full network isolation. If you want to treat sphere computation as a lightweight utility rather than a service to babysit, dsh-sphere is built for exactly that use case.

Use Cases

  • Calling sphere geometry operators inside a local offline inference pipeline
  • Interactively verifying sphere formulas and parameters in a teaching demo
  • Debugging inference in a sandbox with strict network isolation
  • Running network-free geometry steps within a CI pipeline

Best For

  • Inference engineers who need zero-dependency geometry primitives
  • Developers building inference pipelines in air-gapped environments
  • Educators and demo creators who need instant in-session sphere math