No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by toolclub · Agent Tool · ★ 219
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dsh-agent-team-gui safe to install? View the security audit →
dsh-agent-team-gui English | 简体中文 Persistent, reusable multi-model Agent teams for DeepSeek Harness. Give each member its own model, role, fallback route, token limit, and tool policy. Select a saved team beside the normal composer; the lead model plans the work, runs a bounded dependency graph, and synthesizes the result. Why this plugin A team is a reusable product object, not a one-off dispatch form. Create it once in Settings → Teams, then use it across projects and conversations. | Dynamic
| Stars | 219 |
| Forks | 4 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 75.249310198222/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-21 |
| Created | 2026-08-15 |
| Platforms | node |
| Est. Tokens | ~18k |
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dsh-agent-team-gui is Persistent multi-model workflow teams for DeepSeek Harness — dynamic lead planning, bounded DAGs, per-agent model/tools, Run Center and Token insights.. It is categorized as a Agent Tool with 219 GitHub stars.
dsh-agent-team-gui is primarily written in TypeScript. It covers topics such as agent, agent-orchestration, ai-agents.
You can find installation instructions and usage details in the dsh-agent-team-gui GitHub repository at github.com/toolclub/dsh-agent-team-gui. The project has 219 stars and 4 forks, indicating an active community.
dsh-agent-team-gui is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dsh-agent-team-gui on Agent Skills Hub include wesight, MisakaNet, Network-AI. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: