dsh-need-finder
Run the following command in DeepSeek Harness:
dsh plugin install 863683348/dsh-need-finder
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install 863683348/dsh-need-finder in the DeepSeek Harness terminal to install this plugin; the source repository is available at https://github.com/863683348/dsh-need-finder
About this plugin
The DSH plugin catalog lists over a thousand entries by name and category, but your brain never thinks in plugin names. You think in tasks: notify me when a job finishes, view DSH remotely on my phone, scrape a webpage for the agent. dsh-need-finder bridges that gap. Tell the agent what you need in plain Chinese or English, and it returns the best-matching plugin from a curated set of 84, with a bilingual description, a matching rationale, and the install command. Order a dish, do not browse an aisle.
The matching engine is entirely local. No network calls, no LLM inference, no external dependencies. A weighted scoring algorithm hits on name (+3), tag (+2), and description (+1), with Chinese 2-gram substring matching and English tokenization, plus a category dictionary as a safety net. The catalog covers 14 categories across 84 real plugins, and the data file is plain JSON you can extend or trim on the fly.
Beyond single-plugin discovery, dsh-need-finder ships eight community recipe bundles (notification suite, security audit, remote mobile, dev tools, memory set, IM bridge, vision lab, research stack). One apply action outputs a dependency-ordered install plan that workflows and orchestrators can consume directly.
If you describe what you want to do rather than which plugin to install, dsh-need-finder is your starting point.
Use Cases
- Describe a task in plain language and let the agent recommend the best-fit plugin with an install command
- Apply a curated recipe bundle (notification suite, dev tools, remote mobile) to output a dependency-ordered install plan
- Match needs across 14 categories from 84 curated plugins with zero network calls and zero LLM inference
Best For
- DSH users who describe needs in natural language rather than plugin names
- Developers who want a full plugin environment set up in one pass instead of browsing the catalog
- New users who prefer semantic discovery over memorizing plugin names
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