RuoYi AI is a full-stack enterprise platform for building AI assistants and agents that combine language models, knowledge bases, visual workflows, and multiple cooperating agents. Developers and organizations use it to manage model providers, retrieve information from documents, connect tools through MCP, and orchestrate agent workflows. The catalogue includes skills for working with RuoYi AI.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ageerle/ruoyi-ai --skill character-art-directiongit clone --depth 1 https://github.com/ageerle/ruoyi-aiWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ageerle/ruoyi-ai/character-art-direction)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/character-art-direction"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/character-art-direction/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/character-art-direction"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/character-art-direction.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.00782 |
| Opus 5.5 | $0.00006 | $0.00313 |
| Sonnet 5.5 | $0.00003 | $0.00156 |
| Haiku 4.5 | $0.00002 | $0.00078 |
Grade A, and why
character-art-direction scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
人物美术
角色的戏份决定描述精度，不决定颜值、衣料价格或年龄。主角也可以有普通而可辨认的脸；不把成熟短剧等同于偶像精修、堆砌脏污或昂贵服装。
身份与妆造
先锁定用户给定的年龄、体态、脸部比例、发际线和已存在的辨识点，再写发型、服装剪裁、材质、配饰及穿用状态。同剧人物应有不同的轮廓、眉眼组合和衣料；不要全员同一种精致脸。已有参考图优先，不以增加细节为由换脸、改年龄、新增疤痕或换伤侧。 材质与维护服从年代、身份和具体生活方式。说明布料厚薄、织理、接缝、受力褶皱，以及有依据的磨损位置；工作服也有完整剪裁，不是统一粗布套装。修整程度可以不同，不自动把穷人、匠人和中年人画成污秽或丑陋。 将性格转化为可见且克制的目光、眉间、嘴角、肩颈习惯。身份卡记录稳定特征，表演另属具体镜头；不把愤怒、流泪等临时状态固定为角色的永久外貌。 所有细节服从项目视觉风格。写实采用自然成人肤质、细微年龄痕迹、真实毛发与布料；二维动漫保留该画风的线稿和上色，不强加摄影毛孔、镜头虚化或真人皮肤。 国风三维动画以三维角色建模、CG发束、动画皮肤和PBR衣料表达体积；旧描述或摄影参考里的真人毛孔仅提供年龄和脸部比例线索，不能覆盖项目材质风格。明确的身份绑定修订候选可纠正指定服装与材质，保持脸部身份和所有未指定特征；同一候选各视角使用修订后的同一裁片，不把被拒照片的服装锁成批准连续性。
两类图的用途
身份参照图：清晰脸部与全身服装，柔和中性光；不同视角都是同一人，脸、发际线、服装裁片、配饰左右侧一致。背景仅供识别，不堆环境物；禁止强烈色光遮脸、动作表演、字幕和标签。 影视定妆图或剧情首帧：单幅画面，绑定身份参照，按剧本目标、视线对象与当前时刻表演。说明主光来源、可读的暗部、景别、焦点和空间层次；光线改变，脸部比例、衣料和辨识点不改变。不能把三视图姿势和白底棚拍复制进叙事画面。
看实际图再验收
逐项查看：年龄和选角是否贴合角色；脸与眼睛是否自然；头发是否像假发；织理与褶皱是否服从衣料；衣领袖口及左右标志是否连续；光源是否可解释；人物是否在做本镜的事。文字规范通过或图片任务完成，都不等于视觉合格。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 26 lines · 16 tokens per session scan A 2e99ca098a18
character-art-direction is a skill published in the GitHub repository ageerle/ruoyi-ai (5,756 stars, last pushed 3d ago), licensed MIT. It adds 16 tokens to every session and 782 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-04.
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