nuyoah-xiezhen-prompt — security grade SAFE, quality 52/100

Security audit verdict: SAFE · quality 52/100

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 nuyoah-ai-works · Agent Tool · ★ 730

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is nuyoah-xiezhen-prompt safe to install? View the security audit →

About nuyoah-xiezhen-prompt

南鸢写真提示词 南鸢写真与人像摄影 Skill。支持完整中文 Prompt、系列母版、同系列变体、网页端抽卡关键词、参考图拆解,以及“换一位人物、按同一摄影企划重新拍摄新分镜”的摄影方案复拍模式。 安装 更新已有安装: 更新后新建任务或重新加载 Agent,让新版指令进入上下文。 它解决什么 普通请求默认只交付 Prompt。只有用户明确要求生图时,摄影方案复拍模式才会在 Codex Image 中生成指定数量的图片并逐张检查。完整 Prompt、参考职责、质量状态和追溯记录直接在对话中交付;Eagle 只是检测可用后执行的可选归档,未安装或不可用不会中断任务。 摄影方案复拍会把人物身份与本张表情分开控制:身份图只提供稳定五官锚点,目标场景中的具体事件负责眉眼、呼吸、嘴角、视线和身体反应;同时按参考图重建主体清晰区、主导大形、次级细节和低细节留白,避免把“场景丰富”误写成“背景每一层都清楚”。 用户要求重做时,每一轮都会回到原始人物图与对应写真参考,从摄影方案和本轮反馈重新编译完整 Prompt。上一轮生成图只用于判断哪里失败,不作为新一轮输入;新图也直接对照原作验收,不以“比上一轮好”代替风格保真。 原作若依赖欠曝、失焦、堵黑和偶发抓拍,复拍会额外检查模型是否自行补入连续暖色轮廓光、稳定脸部焦点、皮革塑形、正面回填或英雄式构图,并把这些商业抛光改写为原作支持的曝光与焦点关系;精致 editorial 原作不会被统一做脏。 这不是换脸工具,也不承诺像素级复刻。它重建的是可观察的摄影方案:光源、机位、曝光、背景信息层级、人物事件和成像质感。结果仍受生成模型、参考图质量与人物授权影响。 署名与归档 默认不在图片中加入 或任何其他署名、水印。 只有用户明确要求并给出署名文字时,才把该文字写入 Prompt。 Eagle、个人知识库和其它本地 Skill 都不是使用本 Skill 的前提;Eagle 归档成功时会额外返回 Eagle ID,失败时仍保留完整的对话交付。 普通提示词 摄影方案复拍 文件结构 :触发、授权、输出和跨环境边界。 :提示词模板与摄影方案复拍工作流。 :触发、皮肤反射和复拍回归案例。 :公开发行与行为合同测试。 :公开版本变化。 能力边界 只要 Prompt 时不会擅自生图。 明确要求生图但环境没有图片生成工具时,会交付完整 Prompt 并说明未执行生成,不伪造结果。 Eagle、个人知识库和其他本地 Skill 都不是运行前提。 使用参考图和人物图前,请自行确认版权、肖像权、商标与平台规则。 完整规则见 [SKILL.md](SKILL.m

Quick Facts

Stars730
Forks81
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score51.6607338183999/100
Last Updated2026-08-26
Created2026-07-29
Platformspython
Est. Tokens~4k

Compatible Skills

These tools work well together with nuyoah-xiezhen-prompt for enhanced workflows:

  • nsfc-benzi-audit — semantic(0.53)+complementary+same_lang+similar_pop+shared_platform (69%)
  • native-subtitle-quote-image — semantic(0.41)+complementary+same_lang+similar_pop+shared_platform (65%)
  • femboy-skill — semantic(0.31)+complementary+same_lang+similar_pop+shared_platform (61%)
  • dao-skill — semantic(1.00)+same_lang+similar_pop+shared_platform (60%)
  • universal-examprep-skill — semantic(0.27)+complementary+same_lang+similar_pop+shared_platform (60%)

nuyoah-xiezhen-prompt alternative? Top 6 similar tools

Looking for a nuyoah-xiezhen-prompt alternative? If you're comparing nuyoah-xiezhen-prompt with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

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Frequently Asked Questions

What is nuyoah-xiezhen-prompt?

nuyoah-xiezhen-prompt is 南鸢写真提示词 Agent Skill. It is categorized as a Agent Tool with 730 GitHub stars.

What programming language is nuyoah-xiezhen-prompt written in?

nuyoah-xiezhen-prompt is primarily written in Python.

How do I install or use nuyoah-xiezhen-prompt?

You can find installation instructions and usage details in the nuyoah-xiezhen-prompt GitHub repository at github.com/nuyoah-ai-works/nuyoah-xiezhen-prompt. The project has 730 stars and 81 forks, indicating an active community.

What license does nuyoah-xiezhen-prompt use?

nuyoah-xiezhen-prompt is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to nuyoah-xiezhen-prompt?

The top alternatives to nuyoah-xiezhen-prompt on Agent Skills Hub include playwright-skill, chinese-novelist-skill, skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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