v1.1.8 · MIT
DSH 桌面消息提醒插件:模型需要你操作(审批/方案确认/提问的可操作提醒)或回复在后台完成时,系统通知+提示音+标签页标题提醒你;可在设置页开关并切换通知语言。Desktop notification & message alerts for DeepSeek Harness web — toasts, sounds and a tab-title marker when the agent needs your input (approval / plan review / question) or
v0.5.2 · MIT
Third-party provider reasoning-effort AND input-modality settings for DeepSeek Harness: thinking levels and image-input support declared per model, auto-adapted from a model knowledge base + wire-protocol inference, edited right inside the official Models
— · Apache-2.0
Detects prompt injection by its effect on a sacrificial canary model, not just pattern matching: untrusted input hits a powerless model first, a behavioral check reads the residue, and it returns block, flag, or pass before your primary model acts. Inbound preflight sensor, not a guarantee.
v0.12.2 · MIT
Command Code provider plugin for DeepSeek Harness (dsh). Adds Command Code model access, live model catalog, plan-aware model selection, reasoning effort, image input, web search, and multi-account support.
v3.34.6 · MIT
Framework-aware code intelligence MCP server — 88 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity
v3.34.6 · MIT
Framework-aware code intelligence MCP server — 88 framework integrations, 81 languages, 72.7% fewer input tokens to review a pull request, comprehension at parity
v0.3.3 · MIT
为 DeepSeek Harness 提供电脑控制插件:新鲜 Accessibility 观测、过期状态拒绝、作用域权限与安全输入(目前支持macos)|Accessibility-first macOS Computer Use bundle for DSH with fresh observations, stale-state rejection, scoped permissions, and safe input.
v3.36.9 · AGPL-3.0-only
Local dashboard for Claude Code and Codex CLI sessions: which Claude Code session needs input, every tool call and Claude Code subagent live, token cost and quota, several Claude accounts. Run npx ccdeck.
v4.1.11 · MIT
MCP server that gives AI assistants eyes and hands in the Godot editor: scene editing, input injection, deterministic game-time control, and live runtime state for agent-driven playtesting