DeepSeek Harness Task Notification Plugin: @idoall/dsh-notify
`@idoall/dsh-notify` is a plugin for DeepSeek Harness designed to address the lack of intuitive task-status feedback in long conversations. It offers enhanced and soft notification styles, a collapsible stack, direct navigation, and sound feedback. Notifications are triggered only when the Agent enters an idle state, and users can respond directly within the card. The plugin registers only a page overlay, stores data in memory, and requires DSH version `>=0.1.7-rc.1` and `<0.3.0`.
Read Moreshared-handoff-dsh: Cross-Platform Task State Management and Session Handoff for DeepSeek Harness
`shared-handoff-dsh` is a DeepSeek Harness skill plugin designed to address the continuity challenges of repository-level state and session context. It encapsulates the core workflow of `shared-handoff-kit`, providing a zero-dependency, cross-platform automation solution. The plugin's core features include evidence-based session handoff and recovery, as well as repository-local task ID state management. It supports quickly starting tasks, binding sessions, or exporting context through conversational commands (such as `task-id-bootstrap` and `handoff`). Its automation mechanism is implemented via the DSH event system, requiring no manual intervention and compatible with the state layouts of Codex and Claude. The plugin natively supports Windows 10/11 differences and includes compression.
Read Moredsh-evidence-task-board: DeepSeek Harness Persistent Task State Primitives
`dsh-evidence-task-board` is a DeepSeek Harness (DSH) plugin maintained by MkaliezZ, designed to address pain points around task state persistence and evidence traceability. It acts as a deterministic primitive for state management, offering core capabilities such as creating tasks, changing states, recording evidence, and generating SHA-256 digests, with support for readable rendering. The plugin relies on persistence mechanisms explicitly provided by the host environment for data storage and is intended for scenarios requiring explicit management of task states.
Read More