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 Anionex · Agent Tool · ★ 887
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is dsh-vision-toolkit safe to install? View the security audit →
DSH Vision Toolkit DSH Vision Toolkit brings into DeepSeek Harness as a native Profile Bundle. Give text-only DSH agents eyes—and keep vision in the harness—with intent-aware image Q&A, OCR, original-pixel grounding, UI restoration, pixel verification, managed Artifacts, and Web Settings. Ten independent tools replace shell glue with structured schemas and Agent-scoped progressive exposure. Upstream toolkit: Anionex/agent-vision-toolkit · Project website: [agent-vision.anionex.me](h
| Stars | 887 |
| Forks | 47 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 73.736600822617/100 |
| Last Updated | 2026-10-01 |
| Created | 2026-08-13 |
| Platforms | browser, node |
| Est. Tokens | ~22k |
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dsh-vision-toolkit is [dsh]为纯文本模型设计更强大的视觉工具箱：一行安装使用、粘贴图片直接识别、多张图片问答、截图到前端UI 还原等｜DeepSeek Harness-native integration for agent-vision-toolkit: image Q&A, long-screenshot OCR, UI restoration, grounding, pixel diff, Artifacts. It is categorized as a Agent Tool with 887 GitHub stars.
dsh-vision-toolkit is primarily written in TypeScript. It covers topics such as agent-skills, agent-vision-toolkit, computer-vision.
You can find installation instructions and usage details in the dsh-vision-toolkit GitHub repository at github.com/Anionex/dsh-vision-toolkit. The project has 887 stars and 47 forks, indicating an active community.
dsh-vision-toolkit is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to dsh-vision-toolkit on Agent Skills Hub include watch-skill, modlens, wesight. 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: