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 script-refinementgit 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/script-refinement)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/script-refinement"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/script-refinement/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/script-refinement"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/script-refinement.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 | $0.00027 | $0.00495 |
| Opus 5.5 | $0.00011 | $0.00198 |
| Sonnet 5.5 | $0.00005 | $0.00099 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
script-refinement 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
剧本精细打磨
先建立项目资料台账：来源、可观察事实、用户设定、推断、未核实项。没有检索工具或原文时，不能声称查过链接、史料或完成事实核验。用户提供的原文也可能包含矛盾，必须保留冲突与待核实项，不自行当成正史。
世界观先定义时间地点、人物目标与关系、资源与时间限制、制度阻力、能力上限与代价、服饰建筑道具、不可改动事实。区别历史事实与架空分歧点。技术优势表现为有限条件下的试错与协作，不抹去原有工匠知识，不把所有敌人与官员写成愚蠢工具人。
初版须是有开场、发展、选择、结果及下一集钩子的完整剧本。每场标预计时长，大纲负责因果和节奏，正文只写主要行动和必要对白。摄影、站位和逐秒节拍留给分镜。系统信息只支持主角决策，不能代替主角查证、劳动、承担责任。
精修按本轮问题修改：事实/世界观、因果、人物动机、对白、时长、可拍性。保留没有问题的结构，禁止每轮无故重写或扩长。不得把人物关系和资料疑点替换为廉价情绪口号。每场遵守触发→反应→查证/尝试→选择→后果。
视频能力只用于制作：参考图固定人物服饰与空间，参考音频限定音色与节奏；长片段只在完整动作可持续时使用。参考素材按实际提交顺序编号并明确用途，不默认30秒镜头，不假设成片已通过视觉验收。精修完成后停在剧本，资料尚未核实时不得把推断包装为事实。战争情节保持叙事层面，不提供可照做的武器、爆炸物配方或制造工序。
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 · 17 lines · 27 tokens per session scan A 172509c8e03a
script-refinement is a skill published in the GitHub repository ageerle/ruoyi-ai (5,756 stars, last pushed 3d ago), licensed MIT. It adds 27 tokens to every session and 495 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.
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.…