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-short-drama-writinggit 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-short-drama-writing)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/github-short-drama-writing"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-short-drama-writing/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-short-drama-writing"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-short-drama-writing.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 | $0.00786 |
| Opus 5.5 | $0.00000 | $0.00314 |
| Sonnet 5.5 | $0.00000 | $0.00157 |
| Haiku 4.5 | $0.00000 | $0.00079 |
Grade A, and why
github-short-drama-writing 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 — 26 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
微短剧 · 选题、人物与分集
选题与人物
从已确认故事、受众和基调确定叙事承诺。先明确主角想得到什么、遇到何种阻力、失败损失和有代价的选择；角色身份、动机、关系与知情边界从故事中建立。
分集方法
总集数和单集长度来自项目，不能默认五十至一百集。大纲交代每集起因、人物行动、局面变化和下一集的问题。悬疑、关系、情绪、危机和信息都可构成钩子，选择能被后文兑现的一种，不机械每集强行打脸。 阻力可来自职责、利益、认知或现实条件，不要求所有题材采用四层反派。人物以不同策略争取自己的目标；对手有回应与能力，主角优势要在行动和后果中体现。 单集正文让对白、行动和回应推动关系，压缩重复介绍和无变化工序。开场尽快兑现该集要解决的问题，保留理解所需铺垫；结尾落在真实变化及其影响上。
修订与出海
检查承诺是否兑现、铺垫是否被看见、人物是否突然知道新事实、对白是否符合当时意图。出海改编核对文化背景、人物关系和口语，而非只翻译台词；具体平台要求另查当前来源。局部改稿保留其他已批准情节和演员版本。
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。
场景进出衔接
开门只改变通行条件，不等于人物已经进屋或出屋。涉及进出时，正文写清人物原在门的哪一侧、跨过门口到哪里，以及到位后才发生的事；导演阶段再落实机位和门扇方向。后续离场由它自己的剧情原因触发，不提前混入入场动作。局部返修保持原片其他对白、动作和场序，正文按标题、动作、逐句对白分段留空行；未获当前用户指定不补目标时长。
已定稿剧本交给分镜
用户只要求重写分镜时，把当前确认的完整剧本作为唯一剧情与对白依据，不触发再次改编或重写台词。按场保留事件顺序、角色知识、衣装和关键物件变化；旧候选分镜及参考案例只可用于发现问题，不能覆盖当前原文。编剧风格负责故事与台词，镜头格式和运镜由默认导演规范及选中的唯一导演风格处理。
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 · 30 lines · 0 tokens per session scan A 7fecf801ee37
github-short-drama-writing 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 786 tokens. A static security scan graded it A with 0 findings. It is 100% identical to github-anime-aesthetics, differing in 26 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.