dsh-buenos-aires-city: Buenos Aires City Information Query
The DSH Harness (DSH) provides a plugin architecture to enhance agent capabilities. `dsh-buenos-aires-city` is an extension plugin maintained by user `uckkk`, designed to enable agents to retrieve static information about Buenos Aires City. The installation command is `dsh plugin add github:uckkk/dsh-buenos-aires-city`. This plugin runs with process-level permissions; users should review its source code and MIT license for security before use.
Read Moredsh-grenada: A Geographic Information Query Plugin for Grenada
The DeepSeek Harness (DSH) plugin dsh-grenada localizes basic geographic information for Grenada, aiming to reduce the latency and costs caused by agents frequently calling external APIs. Maintained by uckkk, it requires no extra configuration and can be used immediately after installation to invoke the `country_info` tool in a conversation to retrieve information such as the capital, population, currency, and continent. As a pure Node.js implementation, it does not rely on network requests and is well suited for scenarios requiring fast lookups of static data.
Read Moredsh-geography-oceans: The Five Oceans
The `dsh-geography-oceans` plugin is maintained by uckkk and designed for geographic information processing in DeepSeek Harness (DSH). It provides static data lookup for the five major oceans, supporting `list_ocean` to list entries and `get_ocean` to retrieve details by ID or name. Its core advantage is a pure Node.js implementation with no network dependencies, enabling fast local retrieval of results, suitable for scenarios sensitive to network latency. The installation command is `dsh plugin add github:uckkk/dsh-geography-oceans`.
Read Moredsh-arcgis-pro-bridge: Connecting ArcGIS Pro to DSH via MCP
`dsh-arcgis-pro-bridge` is a plugin for DeepSeek Harness (DSH) that bridges ArcGIS Pro Python logic into DSH, enabling models to directly operate on local GIS data through standard MCP tools. It is pure ESM with vendored Python, has no build step, and supports installation via GitHub. Its core features include structural reading, spatial analysis (such as Buffer and Clip), and execution of custom ArcPy code. It requires a Windows environment, and ArcPy runs within the Python bundled with ArcGIS Pro without polluting the Node.js environment. Users should note that it is limited to local execution and that `execute_arcpy_`
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