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 liangdabiao · Agent Tool · ★ 66
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is deepseek-v4-flash-vision-video-rag safe to install? View the security audit →
DeepSeek V4-Flash Vision Video RAG 让 AI 真正"看懂" 一段视频，然后你对它提问：它告诉你答案、答案发生在 第几分几秒，并切出那一段的可播放片段和关键帧给你核对。 基于 DeepSeek 视觉大模型 的视频理解与问答 （video RAG）agent skill。先按时间轴抽帧阅读、建立索引（一次性），再对问题做 本地粗筛 → 视觉精排 → 深读回答；回答带 时间戳引用，自动生成 自包含 HTML 预览页（内嵌可播放片段 + 关键帧 + 答案），双击浏览器即看。 模型没有音频模态：一切理解基于画面，音轨会被忽略。 使用方法简单： 直接在 codex/workbuddy等下载，然后发送视频给 skill技能，命令完成理解视频 和回答各种问题则可以！ 一、它能做什么 二、实测效果（两个自带 ground truth 的视频） 三、工作原理（通俗版） 和 PDF 版同一个思想：先读一遍做笔记，之后按笔记翻书——只是"页"变成了 "时间段"，"页码"变成了"时间戳"。 第一阶段：看一遍视频，做卡片（ingest，一次性） 视频 ──ffmpeg 抽帧── 按时间顺序的 JPEG 序列 ──上传── DeepSeek 文件服务器 │ ↓ 视觉模型逐帧"观看"（25 帧/批，prompt 声明采样率与时间轴
| Stars | 66 |
| Forks | 5 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 60.2123815745309/100 |
| Last Updated | 2026-08-24 |
| Created | 2026-08-24 |
| Platforms | python |
| Est. Tokens | ~3k |
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deepseek-v4-flash-vision-video-rag is DeepSeek V4-Flash Vision Video RAG 让 AI 真正"看懂" 一段视频，然后你对它提问：它告诉你答案、答案发生在 第几分几秒，并切出那一段的可播放片段和关键帧给你核对。 基于 DeepSeek 视觉大模型 deepseek-v4-flash-vision-exp 的视频理解与问答 （video RAG）agent skill。先按时间轴抽帧阅读、建立索引（一次性）. It is categorized as a Agent Tool with 66 GitHub stars.
deepseek-v4-flash-vision-video-rag is primarily written in Python. It covers topics such as skill, skills.
You can find installation instructions and usage details in the deepseek-v4-flash-vision-video-rag GitHub repository at github.com/liangdabiao/deepseek-v4-flash-vision-video-rag. The project has 66 stars and 5 forks, indicating an active community.
The top alternatives to deepseek-v4-flash-vision-video-rag on Agent Skills Hub include symfony-ux-skills, skillkit, agent-skills. 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: