dsh-commandcode-provider
Run the following command in DeepSeek Harness:
dsh plugin install Victor-770/dsh-commandcode-provider
Paste the following prompt into your AI chat to install this plugin:
In DeepSeek Harness, run the command 'dsh plugin install Victor-770/dsh-commandcode-provider' to install the plugin from the GitHub repository at https://github.com/Victor-770/dsh-commandcode-provider.
About this plugin
This plugin offers DeepSeek Harness users a seamless way to integrate Command Code as an LLM provider. Traditionally, Command Code's Provider API may have access restrictions, such as certain plans like the Go plan lacking API access, but this plugin cleverly bypasses these by using Command Code's own streaming endpoint (/alpha/generate) and API key authentication. This approach ensures that all plans, including the entry-level Go plan, can function as a backend within dsh.
The core capabilities are comprehensive: it auto-mounts upon installation without manual configuration, features intelligent model discovery with offline caching for reliability, and supports reasoning metadata and image inputs for enhanced flexibility. The plugin also includes handy commands for refreshing the model catalog and setting keys, along with optimized error handling to improve the overall user experience.
It's ideal for users looking to expand their LLM options within the dsh ecosystem, especially Command Code holders regardless of their subscription tier. Whether you're a developer, researcher, or everyday user seeking a stable, compatible, and easy-to-deploy plugin to boost dsh's model inference capabilities, this integration is a perfect fit.
Use Cases
- Invoke Command Code models in dsh chat for smooth conversations
- Process image inputs to support visual content analysis
- Rely on caching for stable model catalog access in offline environments
Best For
- DeepSeek Harness users looking to expand model choices
- All Command Code subscribers, including entry-level Go plan users
- AI developers and researchers needing flexible LLM integration
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