dsh-genome

In the AI Agent ecosystem, managing capability modules like skills, MCP, and plugins is often complex and prone to failure, making systems hard to adapt and evolve. dsh-genome, as an evolution engine, addresses this by combining a static resident layer with dynamic experimental bodies, systematizing capability transformation and assembly.

Its core capability lies in implementing a negative feedback servo loop: from metric observation to evaluation and decision-making, then to mutation execution, ensuring progressive stabilization. It automatically senses existing system capabilities, such as auto-absorbing skills and tool metric collection, while offering universal design (zero external dependencies) and stability safeguards (e.g., dead zones, saturation, 30s rollback), all observable and controllable via a Web UI.

This plugin is ideal for developers and AI Agent operators seeking to continuously optimize AI capabilities, as well as users in the DSH framework aiming for automated evolution processes. It enables teams to efficiently manage capability libraries, achieving controlled evolution and integration to enhance overall system reliability.