AI Agent Hub
Back to plugins
🤖

dsh-sign

Model Inference Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-sign

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

Run dsh plugin install uckkk/dsh-sign in DeepSeek Harness to install this plugin; the full source code is available at https://github.com/uckkk/dsh-sign, and once installed you can invoke its registered symbol evaluation tool directly within a session.

About this plugin

At the symbol-evaluation stage of a model-inference pipeline, the operations tend to be tiny but called frequently. Routing every single check through a network call or an external service adds needless latency and makes local workflows brittle. dsh-sign packages symbol judgment into a pure Node.js plugin with zero network dependency and no extra runtime, so you can run symbol-level inference in the leanest possible environment without standing up any backend.

The workflow stays deliberately simple: once the plugin is installed it registers its tool automatically, and you invoke that tool directly inside a session. There is no API wiring, no configuration file, and no outbound request on every call. Every evaluation completes locally in the same process, which keeps both latency and footprint at a minimum.

This makes dsh-sign a good fit for anyone building deterministic, high-frequency symbol operations inside a local inference pipeline — especially when the environment is offline, air-gapped, or simply asked to stay minimal. Released under the MIT license, it can be dropped into any project without usage restrictions or attribution friction.

Use Cases

  • High-frequency, low-latency symbol evaluation inside a local inference pipeline
  • Deterministic inference tasks in offline or air-gapped environments
  • Quickly registering a zero-dependency judgment tool in a lightweight toolchain

Best For

  • Developers building local inference pipelines who want to eliminate network overhead
  • Engineers who prioritize latency sensitivity and prefer pure in-process computation
  • Teams deploying model workflows in restricted or offline environments