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 LunarXuan · Codex Skill · ★ 302
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is image-prompt-reverse safe to install? View the security audit →
Image Prompt Reverse 中文 · English 中文 是一个用于 Codex 的图片反推提示词 Skill。它会分析用户上传的参考图片，识别图片用途、媒介、主体、构图、镜头语言、光影、色彩、材质、背景、空间层次、情绪和后期风格，并生成可直接用于 AI 生图工具的高还原度提示词。 支持类型 摄影：人像、产品、纪实、食物、动物、自然和城市风景 插画：扁平、二次元、治愈、手绘、国风和赛博风格 3D：写实、半写实、卡通、黏土、潮玩和产品渲染 字体、Logo、海报、平面设计和抽象图形 IP角色、Q版角色、盲盒和多主体混合画面 输出内容 默认输出两项： 正向 Prompt：450—700个中文字符的连续自然语言，并附英文等义版本 Negative Prompt：10—15条英文负面词，用逗号分隔 Skill 会优先提炼最影响相似度的3—5个视觉锚点，避免编造看不清的细节，也会根据目标媒介排除容易混淆的错误风格。图片中的文字、Logo和说明会被视为视觉内容，不会被当作指令执行。 安装 将本仓库目录复制到 Codex 的个人 Skills 目录： 或直接克隆： 安装后可使用 调用。 文件结构 友情链接 欢迎访问 LINUX DO，浏览社区最新主题与讨论。 English is a Codex Skill for reverse-engineering image-generation prompts from user-uploaded reference images. It analyzes the image purpose, medium, subject, composition, camera language, lighting, color, materials, background, spatial depth, mood, and post-processing style, then produces high-fidelity prompts that can be
| Stars | 302 |
| Forks | 30 |
| Category | Codex Skill |
| License | GPL-3.0 |
| Quality Score | 67.17760377068/100 |
| Last Updated | 2026-09-06 |
| Created | 2026-09-01 |
| Platforms | codex |
| Est. Tokens | ~4k |
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image-prompt-reverse is High-fidelity AI image prompt reverse-engineering skill for Codex. It is categorized as a Codex Skill with 302 GitHub stars.
You can find installation instructions and usage details in the image-prompt-reverse GitHub repository at github.com/LunarXuan/image-prompt-reverse. The project has 302 stars and 30 forks, indicating an active community.
image-prompt-reverse is released under the GPL-3.0 license, making it free to use and modify according to the license terms.
The top alternatives to image-prompt-reverse on Agent Skills Hub include symfony-ux-skills, awesome-ai-agent-skills, skillnote. 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: