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-production-methodsgit 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-production-methods)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/github-production-methods"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-production-methods/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-production-methods"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/github-production-methods.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 | $0.00022 | $0.00225 |
| Opus 5.5 | $0.00009 | $0.00090 |
| Sonnet 5.5 | $0.00004 | $0.00045 |
| Haiku 4.5 | $0.00002 | $0.00022 |
Grade A, and why
github-production-methods 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.
What it actually says
GitHub 制作方法 · 阶段状态与最小返修
在现有内容审阅中补充阶段与证据判断，不改变输出结构。计划、实际素材、已提交、已生成与已审阅分别确认；图片提示词不证明已有图片，任务终态不证明影片合格。 失败记录具体镜号、可见或可听观察、影响和最小修复范围。返修回到所属阶段，不重做其他已批准演员、图片、声音和镜头。未知付费提交先查原任务，保留编号、参数和原片，不自动重投。 成片检查区分解码、代表帧、文本/ASR、完整运动观看与试听；剪辑记录真实入出点和原片、导出版差异。未检查的内容保持待验，热度、固定模板和目标时长不作为质量证明。
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 · 11 lines · 22 tokens per session scan A 156e5235cfbe
github-production-methods is a skill published in the GitHub repository ageerle/ruoyi-ai (5,773 stars, last pushed 2d ago), licensed MIT. It adds 22 tokens to every session and 225 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 2026-10-21.
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mindmap
Create concise hierarchical mind maps as canonical Markdown with a static PNG download.