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dsh-companion-enterprise

Workflow Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install beijingwahw/dsh-companion-enterprise

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

To install this plugin in DeepSeek Harness, please execute the installation command in your terminal. The plugin source code is located at https://github.com/beijingwahw/dsh-companion-enterprise.

About this plugin

In enterprise AI integration, development teams often face challenges regarding security, compliance, and collaboration efficiency. DeepSeek Companion Enterprise aims to address these issues by building upon DeepSeek Harness to provide a secure platform with zero telemetry and local-only data storage, offering a comprehensive capability matrix ranging from security auditing to team collaboration.

The core strength of this plugin lies in its powerful automation and governance capabilities. It features built-in Data Loss Prevention (DLP) and prompt injection detection to safeguard enterprise assets. Through "automatic experience distillation," it mines "error-to-fix" insights from historical sessions to assist team growth. Additionally, it supports task orchestration, self-healing execution, and adaptive model routing to help developers optimize model performance and control API costs.

Whether for large-scale corporate R&D teams or agile groups seeking efficient collaboration, DeepSeek Companion Enterprise provides full-stack support from security compliance to intelligent collaboration. It is ideal for developers requiring precise AI workflow management, model drift monitoring, and complex orchestration, empowering organizations to build secure and reliable AI engineering pipelines.

Use Cases

  • Build an enterprise-grade AI security audit and compliance platform
  • Optimize multi-model call costs and performance monitoring
  • Accumulate team knowledge base and collaboration experience

Best For

  • Security teams requiring strict data leakage prevention and prompt injection detection
  • Developers pursuing API cost control and model drift monitoring
  • Tech leads building automated AI workflows