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dsh-future-synthetic-food

Model Inference Updated 2026.08.21

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-future-synthetic-food

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

Run dsh plugin install uckkk/dsh-future-synthetic-food in the DeepSeek Harness terminal to install this plugin; the source code is available at https://github.com/uckkk/dsh-future-synthetic-food .

About this plugin

When you are working inside a model-inference session and need to quickly browse the full synthetic-food catalogue or look up a single entry by its id or name, dsh-future-synthetic-food provides two ready-to-use local tools. list_synfood enumerates every synthetic-food record, while get_synfood returns the details of one entry keyed by id or name, so data lookups inside a conversation no longer require an external service or a network round-trip.

The plugin is implemented entirely in Node with zero runtime network dependencies. It is a natural fit for offline sandboxes, air-gapped inference pipelines, or edge environments where every extra dependency and every HTTP call is a liability. Data is loaded once, queries are synchronous, and there is no timeout, no auth handshake, and no third-party failure surface to worry about.

If you are building AI applications in food science, future dining, or synthetic nutrition and need a lightweight, protocol-free way to reference structured food entries during inference, this plugin fits the bill. It is MIT-licensed and works out of the box once installed.

Use Cases

  • Quickly browse the full synthetic-food catalogue inside an inference session
  • Look up a specific synthetic-food entry by id or name
  • Retrieve structured food data in air-gapped or offline environments without any network calls

Best For

  • Developers building food-science or synthetic-nutrition AI applications
  • Engineers working in air-gapped or intranet inference pipelines who need local data access
  • AI engineers who need to reference structured food entries during model conversations