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dsh-prune-guide

Model Inference Updated 2026.08.21

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-prune-guide

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

Run dsh plugin install uckkk/dsh-prune-guide in the DeepSeek Harness terminal to install this plugin; the source repository is at https://github.com/uckkk/dsh-prune-guide

About this plugin

Plant care and pruning advice is typically scattered across dozens of web pages, forum threads, and short videos. Every time you need a quick answer on whether to trim a plant right now and by how much, you end up scrolling the web, hoping the snippet you find actually matches your current season, light conditions, and species. dsh-prune-guide collapses that into a single parameterized tool call inside your DSH session: pass in the relevant parameters and receive a tailored pruning and care plan.

The plugin keeps things deliberately lean. The entire implementation is pure Node.js with zero network dependencies, so every lookup resolves locally and instantly. There are no API timeouts, no rate limits, and no external service to keep alive. It starts fast, stays out of the way, and behaves identically in an offline lab as it does on a Wi-Fi connected desk.

It is a good fit for two audiences. First, the home gardener who would rather get a structured pruning recommendation in chat than open yet another browser tab before every trim. Second, the developer building DSH workflows who needs a small, deterministic, fully offline reference step that introduces no heavyweight dependencies or extra services into the pipeline.

Use Cases

  • Query pruning and care recommendations for a specific plant by parameters in a DSH session
  • Retrieve structured plant care plans instantly in an offline environment
  • Embed plant maintenance steps into automated workflows without network dependencies

Best For

  • Home gardeners who want quick pruning guidance without opening a browser tab
  • Developers building DSH workflows that demand zero network dependencies and no external services
  • Terminal users who prefer localized, lightweight tooling with no extra runtime overhead