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dsh-gov

admin-security Updated 2026.08.25

Run the following command in DeepSeek Harness:

dsh plugin install 863683348/dsh-gov

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

Install the plugin in DeepSeek Harness by running dsh plugin install 863683348/dsh-gov; source is available at https://github.com/863683348/dsh-gov .

About this plugin

When enterprise teams bring DSH into production, the first three questions are always the same: who is allowed to let an agent call high-risk tools, how do you trace an incident, and where does the token spend land. dsh-gov bundles all three into a single plugin, keeping governance logic decoupled from business code while staying completely non-blocking for agent execution.

The core capabilities revolve around three modules: policy gates let you configure allow, deny, or ask rules per tool, agent, or workspace, with wildcards and priority for flexible composition, failing closed (deny over ask over allow) on ties to guarantee a safety floor; every tool invocation decision and result is appended to a structured JSONL audit log, filterable by agent, tool, or time window, and exportable straight into compliance pipelines; token quotas plug into the host meter, setting day, week, month, or total budgets per agent, with automatic context warnings on exceedance and one-click resets.

If you are building enterprise-grade permission boundaries, audit trails, or cost guardrails for DSH, and you want these capabilities to live in zero-dependency, unit-testable logic layers, work globally across workspaces, and never block an agent on failure, dsh-gov is the layer you need.

Use Cases

  • Setting permission boundaries for high-risk tool invocations by DSH agents
  • Compliance teams exporting structured audit logs for incident tracing
  • Platform teams setting periodic token usage budgets per business agent

Best For

  • Platform engineers onboarding DSH into enterprise production
  • Security or compliance teams handling audit and incident tracing
  • Tech leads managing AI agent operational costs