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 xlsxgit 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/xlsx)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/xlsx"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/xlsx/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/xlsx"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/xlsx.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Prompt Injection · line 98 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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 | $0.00096 | $0.03276 |
| Opus 5 | $0.00048 | $0.01638 |
| Sonnet 5 | $0.00019 | $0.00655 |
| Haiku 4.5 | $0.00010 | $0.00328 |
Grade A, and why
xlsx 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 10d 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
80% identical to xlsx — 112 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.
How it starts
The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Requirements for Outputs
All Excel files
Zero Formula Errors
- Every Excel model MUST be delivered with ZERO formula errors (#REF!, #DIV/0!, #VALUE!, #N/A, #NAME?)
Preserve Existing Templates (when updating templates)
- Study and EXACTLY match existing format, style, and conventions when modifying files
- Never impose standardized formatting on files with established patterns
- Existing template conventions ALWAYS override these guidelines
Financial models
Color Coding Standards
Unless otherwise stated by the user or existing template
Industry-Standard Color Conventions
- Blue text (RGB: 0,0,255): Hardcoded inputs, and numbers users will change for scenarios
- Black text (RGB: 0,0,0): ALL formulas and calculations
- Green text (RGB: 0,128,0): Links pulling from other worksheets within same workbook
- Red text (RGB: 255,0,0): External links to other files
- Yellow background (RGB: 255,255,0): Key assumptions needing attention or cells that need to be updated
Number Formatting Standards
Required Format Rules
- Years: Format as text strings (e.g., "2024" not "2,024")
- Currency: Use $#,##0 format; ALWAYS specify units in headers ("Revenue ($mm)")
- Zeros: Use number formatting to make all zeros "-", including percentages (e.g., "$#,##0;($#,##0);-")
- Percentages: Default to 0.0% format (one decimal)
- Multiples: Format as 0.0x for valuation multiples (EV/EBITDA, P/E)
- Negative numbers: Use parentheses (123) not minus -123
Formula Construction Rules
Assumptions Placement
- Place ALL assumptions (growth rates, margins, multiples, etc.) in separate assumption cells
- Use cell references instead of hardcoded values in formulas
- Example: Use =B5*(1+$B$6) instead of =B5*1.05
Formula Error Prevention
- Verify all cell references are correct
- Check for off-by-one errors in ranges
- Ensure consistent formulas across all projection periods
- Test with edge cases (zero values, negative numbers)
- Verify no unintended circular references
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.
- 10d ago First seen · 362 lines · 96 tokens per session scan A 5efaceb1c1df
xlsx is a skill published in the GitHub repository ageerle/ruoyi-ai (5,683 stars, last pushed yesterday), licensed MIT. It adds 96 tokens to every session and 3,276 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to xlsx, differing in 112 lines, and is treated as a copy.
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