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 github-manju-directinggit 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/github-manju-directing)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/github-manju-directing"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-manju-directing/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/github-manju-directing"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-manju-directing.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 | $0.00000 | $0.00851 |
| Opus 5.5 | $0.00000 | $0.00340 |
| Sonnet 5.5 | $0.00000 | $0.00170 |
| Haiku 4.5 | $0.00000 | $0.00085 |
Grade A, and why
github-manju-directing 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 3d 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.
This is a copy
100% identical to github-anime-aesthetics — 23 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
漫剧导演 · 空间、动作与表演
空间与表演
读取当前剧本、真实人数、资产版本和起始空间。把人物位置、身体朝向、支撑物与持物状态写成可见关系，摄影机位置另记。画外人物仍保留世界状态，不因裁幅消失而瞬移。 文戏以人物此刻需求决定目光、呼吸、手势和对手反应，微表情与变化时机对应，不给全体人物套同一种悲伤或笑容。对白逐句保留语气和原声源，画外声不错误绑定到画内嘴型。 动作戏先建立距离、接触点、重心、受力方向和结果，再安排景别。冲击、烟尘和碎裂来自明确原因，不能用闪光替代命中，不能用摆拍替代行动。
摄影与段缝
景别必须能承载本镜发生的事件，细节特写不承担需要全身或全空间才能读懂的动作。摄影重点可以从一人移到另一人，不强制首镜单主体；以实际剧情和空间清晰为准。 连续段从上一段末态进入，站位、手物接触和已发生结果保持。换时换地、反应镜或过渡镜需说明作用；不默认每个接缝补画一张图，不靠拉伸、慢动作或空等凑满预算。 负面要求尽量转化为可见的正向执行细节，材料、光源与角色行为具体对应。模型选择、接口语法和参考限制使用本项目目录，不沿用来源中未经当前接口核实的能力对照表。
媒体提示词
以实际人物的站位、支撑、持物和目光起始，写清刺激、选择、动作、接触、受力结果及对手回应。景别让观众看清事件，摄影与光色服务表演；原对白声源准确，动作声音与接触同步。末态保留身体、道具和空间变化供下一段继承。
项目接入边界
本技能提供当前阶段的制作方法，沿用项目现有数据、输出结构、模型目录与内容审阅。用户原文、已批准资产和当前项目决定优先；来源示例不是本剧事实。模型参数与参考语法交给本项目实际供应商适配，不从旧手册推定平台能力。 首帧按需使用，不是生成前置条件；不默认补图。视频正文采用连续导演描述，时长预算独立保存为 timing。写作、修订和技能安装不会提交付费图片、视频、配音或音乐任务。附属脚本仅作文本资料。 修订已有分镜保留已批准图片、连续性、素材任务编号和制作版本；当前只处理有问题的阶段。结构检查、任务完成与成片画面、运动和声音验收分别记录。
门口与空间转换
涉及进出门时，分别确定摄影机所在一侧、人物起点、门扇开合与铰链侧、跨门路线和抵达位置。门可内开或外开，按当前资产与剧情确定；门扇运动不代表人的行进方向。有锁或门闩时先解除，再移动门扇，钥匙和锁的去向延续到下一镜。 让观众看见人物从起始一侧经过门口到目标一侧，或在能核对位置的动作匹配点切镜；不能只写“推门”就切到人物已在房间深处。入场完成后再取物、检查或交谈；后续离场单独保留它的触发与路线。
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 3d ago First seen · 29 lines · 0 tokens per session scan A c4644a0c6f96
github-manju-directing is a skill published in the GitHub repository ageerle/ruoyi-ai (5,773 stars, last pushed 2d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 851 tokens. A static security scan graded it A with 0 findings. It is 100% identical to github-anime-aesthetics, differing in 23 lines, and is treated as a copy.
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.
surfsense-documents
Make or change a Word document (.docx), a PDF, a PowerPoint deck (.pptx) or an Excel workbook (.xlsx) by writing a Python script that surfsenserenderdocument runs, optionally starting from a source's own Word or PowerPoint file. Load it whenever the user asks for one of these, or for a change to one you made.
surfsense-data
Work out numbers from spreadsheet and CSV sources (.xlsx, .csv, .tsv) by writing a pandas script that surfsenseanalyzedata runs: totals, averages, trends, rankings, comparisons, cleaning, and charts that can go into a document. Load it whenever the user asks for figures from a table, or a chart of one.
video
Create polished narrated MP4 videos with Remotion in the sandbox.
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.
Create polished PDF files for requests such as PDFs, resumes, CVs, reports, letters, one-pagers, and printable documents.