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 visual-worldgit 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/visual-world)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/visual-world"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/visual-world/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/visual-world"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/visual-world.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 | $0.00010 | $0.00770 |
| Opus 5.5 | $0.00004 | $0.00308 |
| Sonnet 5.5 | $0.00002 | $0.00154 |
| Haiku 4.5 | $0.00001 | $0.00077 |
Grade A, and why
visual-world 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
场景与人物视觉锁定
用于人物、场景和镜头图片。分别锁定故事年份、建筑装修风格、维护程度、陈设密度；服装与设备服从项目设定。禁止把年代感、情感、父亲遗物自动翻译成破败、霉斑、灰尘、垃圾或废弃空间。 所属项目世界观只作为年代、材料可获得性和发展阶段的约束，不是待画物件清单。本张资产描述决定当前可见事物、规模与生长状态；后续计划、未来装备和未完成的建设不提前出现，多年代故事不混用其他时点的服装与材料。 前现代场景没有已引进的剧情依据时，不添加塑料薄膜顶、塑料育苗盘、现代工业玻璃棚、洋装或西式翻领衬衫。温棚、苗床、作物使用当前描述实际给定的材料、种类与阶段，不将幼苗、初次试种或短期观察画成成熟稻穗、密集丰收田。 贫困和建设初期服从当前描述的小规模、低陈设密度与未完工程，不因“电影级”放大成繁华都市、巨大湖面、豪华铺装或竣工城防；节制不等于一律加破败污损。场景构图呈现实际空间，无依据的地名牌匾、城名字样和招牌不得凭空创造，精确文字留给后期。 场景参考为无人物的空间基准图：明确入口、窗、工作台、主要家具相对位置，光源可解释；陈设分区有序，留出人物活动区。只保留少量有叙事作用的可移动物，避免杂物淹没主角。 人物设定图用于身份和服装，正面视图属于设定用途；叙事首帧必须重新按本镜动作、视线和机位构图，不复制设定图姿势。先遵守用户确认的身份，不能借重新生图换脸、换年龄、换衣服。 上述连续性锁默认适用于已批准素材。请求明确标记为“身份绑定的修订候选”时，参考只提供脸部身份或非人主体的核心辨识点；允许按本轮明确要求纠正材质、服装裁片或指定结构，不沿用被拒参考的对应缺陷。未指定项继续保持，候选经人工选择后才纳入批准连续性。 背景参考锁定建筑与家具，移动道具和门灯状态以本镜为准。手接触的物体应看得见且数量合理。场景不能被误生成人物合影。 图片只画当前时刻，不提前出现后续发现的账本或已完成动作。说明构图焦点和允许出现的物件，不能只堆砌“电影级、悲伤、怀旧”。 国风三维动画应有可见的风格化CG雕塑体块、清晰体面与受控色面，角色发束和布景材质经过设计；不能把仿真人数字人、写真肤质或真人棚拍等同于动画。身份比例与年龄保持，材质风格不靠换脸或夸大五官实现。
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 Changed · +5 lines 8a4e99a2867a
- 15d ago First seen · 13 lines · 10 tokens per session scan A 6cb36f70ebaf
visual-world is a skill published in the GitHub repository ageerle/ruoyi-ai (5,756 stars, last pushed 3d ago), licensed MIT. It adds 10 tokens to every session and 770 once invoked, about $0.0000 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 2026-10-01.
Other skills, from other repositories
wegent-knowledge
Knowledge base management and search tools for Wegent. Provides capabilities to list, create, update, and search knowledge bases and documents using RAG retrieval. Use this skill when the user wants to manage knowledge bases, documents, or search for information programmatically.
knowledge-org
A guide to organising large bodies of knowledge so an AI system can navigate relationships, summaries, and layers instead of searching only flat text chunks. It covers tree indexes, knowledge graphs, file-like structures, incremental updates, and long-term memory layouts.
rag-pipeline
A guide to RAG, a method where a system retrieves relevant passages from an external knowledge base before an AI model writes an answer. It covers splitting documents, searching by meaning or exact words, combining results, and measuring retrieval quality.
memory-rag-poisoning-detection
Detect persistent-memory poisoning and RAG/knowledge-base contamination via dialogue testing. Use when the agent has long-term memory, a user/preference store, or writes to a retrievable knowledge base.
beautiful-article
A tool for turning source material—such as a web page, PDF, DOCX, Markdown file, text, screenshot, or pasted content—into a single offline HTML article.
web-design-engineer
Build or redesign polished browser-rendered visual artifacts with HTML/CSS/JavaScript/React: pages, dashboards, prototypes, slide decks, animations, UI mockups, and data visualizations. Use for visual front-end creation, design-system exploration, design critique, or explicit browser acceptance / QA of a web artifact.…