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dsh-hamster-pick

Client Updated 2026.08.21

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-hamster-pick

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

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

About this plugin

Caring for a hamster, chinchilla, or another small pet often means juggling a dozen parameters at once—diet portion, bedding type, cage dimensions, temperature, humidity. Yet pulling together a quick, tailored care plan for your specific setup still requires digging through forums, bookmarking spreadsheets, and cross-checking species guides. dsh-hamster-pick cuts that friction: supply the parameters that matter to you and receive a small-pet care plan aligned to that exact combination, without the usual scavenger hunt.

The plugin is written entirely in plain Node.js and executes locally on the client side with zero network calls. No API keys, no timeouts, no data leaving your machine, and it behaves identically whether your Wi-Fi is on or off. Once the tool is registered in a session, you simply invoke hamster_pick in conversation and get a structured answer back with minimal latency.

It is a natural fit for DeepSeek Harness users who want small-pet knowledge woven into their agent workflow, and for developers who prefer offline, dependency-free, MIT-licensed local utilities. The scope is intentionally narrow—single capability, clear input and output contract, no backend, database, or third-party service—so maintenance overhead stays low and the plugin is genuinely ready to use out of the box.

Use Cases

  • Tune diet and bedding parameters to instantly get a matching care plan
  • Generate personalized small-pet care advice even on a fully offline connection
  • Invoke small-pet knowledge directly inside a DeepSeek Harness conversation

Best For

  • Everyday pet owners who want fast, targeted small-pet care recommendations
  • Developers who prefer local, zero-dependency, MIT-licensed utilities
  • Agent users integrating domain knowledge into DeepSeek Harness workflows