dsh-future-quantum-computing
Run the following command in DeepSeek Harness:
dsh plugin install uckkk/dsh-future-quantum-computing
Paste the following prompt into your AI chat to install this plugin:
Run dsh plugin install uckkk/dsh-future-quantum-computing in DeepSeek Harness to install this plugin; the source repository is available at https://github.com/uckkk/dsh-future-quantum-computing .
About this plugin
Within the DeepSeek Harness model-inference ecosystem, quantum-computing-related entries are often spread across local knowledge bases with no lightweight, ready-to-use query interface. dsh-future-quantum-computing addresses exactly this gap by wrapping quantum-computing entry retrieval into a set of tools callable directly in a session, so developers no longer need to manually scroll through files or stitch together ad-hoc query logic.
The plugin ships two core tools: list_qc returns the full list of quantum-computing entries in one call, while get_qc lets you look up a single entry by its id or name for detailed inspection. The entire implementation is written in pure Node with zero network dependencies, meaning it runs reliably in fully offline local environments and its response time depends only on data volume, not external latency.
If you are building interactive workflows around quantum-computing knowledge entries in a local session, or simply need a consistent query interface for a small body of structured data, this plugin offers a low-barrier, dependency-free starting point. Its MIT license gives you the freedom to integrate it into your projects and extend it as you see fit.
Use Cases
- Quickly search local quantum-computing knowledge entries in an offline environment
- Look up entry details by id or name directly in a DeepSeek Harness session
- Provide a lightweight list-and-detail query interface for structured quantum-computing data
Best For
- Developers managing quantum-computing entry data in local sessions
- Users seeking zero-network-dependency, fully offline plugins
- Integration developers building knowledge-retrieval workflows in the DeepSeek Harness ecosystem
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