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 safe-refactoringgit 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/safe-refactoring)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/safe-refactoring"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/safe-refactoring/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/safe-refactoring"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/safe-refactoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.00421 |
| Opus 5 | $0.00026 | $0.00211 |
| Sonnet 5 | $0.00010 | $0.00084 |
| Haiku 4.5 | $0.00005 | $0.00042 |
Grade A, and why
safe-refactoring 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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 38 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Safe refactoring
Change structure without losing behavior, protocol validity, or concurrent user edits.
Before mutation
- Establish the exact behavior to preserve and the intentional behavior change, if any.
- Locate callers, implementations, serialized forms, tests, configuration, and external protocol names.
- Check the working tree and treat unrelated modifications as user-owned.
- Define mechanical acceptance checks: compile, focused tests, type checks, contract tests, and diff inspection.
- Split the work into dependency-ordered increments when a single atomic patch would be hard to verify.
Mutation rules
- Use expected content hashes for existing files; a mismatch means reload and reconcile instead of overwriting.
- Prefer narrow edits and stable compatibility adapters while consumers migrate.
- Keep persisted schema and event changes versioned; tolerate additive fields where forward compatibility is intended.
- Preserve tool-call/result adjacency, event IDs, resource ownership, cancellation, and error semantics during agent-runtime refactors.
- Do not mix broad formatting or unrelated cleanup into the functional diff.
- If a generated artifact should change, update its source and regenerate it with the documented command.
Verification loop
- Inspect the resulting diff for accidental deletion, path drift, stale names, and expanded authority.
- Run the smallest fast check that catches syntax/type errors, then the focused behavior tests.
- Run broader integration checks when the changed boundary has multiple consumers.
- Treat warnings or skipped tests relevant to the change as evidence to explain, not automatic success.
- If verification fails, retain the diagnostic evidence, correct the implementation, and repeat.
Completion report
Report the behavioral outcome, compatibility decisions, important changed paths, and checks actually executed. Call out anything not verified. Do not describe a refactor as behavior-preserving without evidence.
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.
- 9d ago First seen · 38 lines · 52 tokens per session scan A 2acb98892779
safe-refactoring is a skill published in the GitHub repository ageerle/ruoyi-ai (5,683 stars, last pushed today), licensed MIT. It adds 52 tokens to every session and 421 once invoked, about $0.0003 per session on Opus 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 2026-08-30.
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