Plugins

Plays a chime when the AI asks a clarifying question (a `?` / `？` in the message, or an `ask_user_question` tool call). Synthesized via Web Audio API, no audio file, works on every OS.

Real Windows OS toasts for DeepSeek Harness: task-finished, needs-input and needs-approval alerts that persist in Notification Center under DeepSeek branding. Windows-only, zero runtime dependencies.

Reminder bubbles beside the input for DeepSeek Harness: any plugin or AI can push reminders (text/link) with chat-style stacking, drag-resize, AI summary, visual customization and random outfit presets. A static profile plugin available in every preset without disappearing on restart.

Task-complete and question notification sounds for the DeepSeek Harness Web UI: a soft chime when the agent finishes a turn, a ding-dong when it asks you something (questions, plan reviews, approvals). Synthesized Web Audio, zero assets, mute toggle and volume slider in the session header and settings.

One-click update for the local DeepSeek Harness checkout — compare versions, pull and rebuild, then restart, and roll back to any past release, all from a sidebar panel with live progress.

DSH Source Control tab in dsh-better-sidebar that mirrors VS Code Git workflows.

Git status badges for DSH: sidebar workspace-row badges and an input-row chip showing the current conversation's workspace git state, with event-driven freshness over SSE.

Git Changes tab for the DSH web GUI: staging, diffing, AI-drafted commit messages, branch/merge/stash/worktree operations and sync, with live updates via fs.watch plus a changeToken poll.

Task Runner mode for DeepSeek Harness: the main agent decomposes large tasks into independent subtasks, dispatches them to subagents (default local model) in fresh short contexts, and synthesizes results; concurrency and worker/orchestrator models are configurable in the settings panel.

Scheduled DeepSeek Harness Agent tasks with mandatory end-to-end tests, run history, and bidirectional delivery through nine messaging channels.

All-in-one combo patch for STARDUSTLC's 18 DSH plugins: one command injects shared default configs across office, media, DevOps and notification presets; each component installs directly via a one-line add command (flat design, no git subdependencies).

Jimu (积木): domain-based orchestration plugin for DeepSeek Harness — domain_plan confirmation, per-domain sub-agents with hand-off, and automatic LLM audit.

✨ One-click prompt polisher for the DSH composer — an agent refines your rough draft in a side chat over multiple turns, and you copy the result back when you're happy.

Turns a product or UI request into an implementation-ready design spec with /ui-workflow and /ui-rerun. Analysts run through a read-only ui_analyst tool, and the lead agent writes docs/ui-workflow reports.

External Agent control plane for Codex, Claude Code, Cursor Agent, and Antigravity adapters with settings and background job visibility.

Intercepts turn-stopping to force one critique-and-improve round after each completed turn before the agent stops.

Multi-agent isolated debate for DeepSeek Harness: debaters of different temperaments research independently (no herding), then cross-examine, then judges converge to a conclusion. Runs as a background job with per-agent progress, token accounting, wave-parallel execution, and session-referenced resume.

Plan-and-execute orchestration over ReactLoopAgent — turns a goal into reviewable steps with a web UI plan card.

Claude Code style /init command and tool that writes a minimal CLAUDE.md and symlinks AGENTS.md to it.

Captures every plan the agent presents through exit_plan_mode as markdown under <workspace>/.dsh/plans, opens a review window, and keeps a Plans history tab recording the outcome of each plan.

Preset workflow template library: 12 reusable orchestration script templates with wf_template_list / wf_template_search / wf_template_run / wf_template_validate tools and static template validation.

DSH session workflows for packaging verifiable capsules, evaluating reusable Skills, and comparing controlled session trajectories.

Input redundancy pruning for DeepSeek Harness — drop repeated/redundant tokens before they reach the model (layer ②).

Context compaction / headroom for DeepSeek Harness — compress the live context so the prompt stays small without losing the state that matters (layer 3).