by vibeinging · MCP Server · ★ 115
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 115 |
| Forks | 4 |
| Language | JavaScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 55/100 |
| Open Issues | 4 |
| Last Updated | 2026-08-15 |
| Created | 2026-08-13 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~13k |
DSH Desktop DSH Desktop 插件集成版(DSH Desktop Bundle Edition)—— 把官方 DSH Web、社区插件和桌面能力装进一个开箱即用的应用。 对话、文件、Git、终端、任务、Worktree 与插件市场,都运行在同一个 DSH Profile 中。 English DSH Desktop(
These tools work well together with deepseek-harness-desktop-app for enhanced workflows:
Looking for a deepseek-harness-desktop-app alternative? If you're comparing deepseek-harness-desktop-app with other mcp server tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Open-source alternative to Claude Cowork — a local-first AI agent desktop app · multi-model · self-evolving sk
CaoGen — a vendor-neutral, local-first AI work desktop. Multi-provider agent runtime, GUI automation, MCP, Git
CaoGen — a vendor-neutral, local-first AI work desktop. Multi-provider agent runtime, GUI automation, MCP, Git
PawWork — free, open-source desktop AI agent for macOS and Windows, built on DeepSeek Harness (DSH). Free mode
SparkWork is a local-first desktop Agent workbench for professional development and complex work. Orchestrate
A modern desktop application for managing Model Context Protocol (MCP) servers across multiple AI agents
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: