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 verificationgit 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/verification)<a href="https://agentmods.dev/skills/ageerle/ruoyi-ai/verification"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/verification/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/verification"><img src="https://agentmods.dev/badge/skills/ageerle/ruoyi-ai/verification.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 | $0.00054 | $0.00604 |
| Opus 5.5 | $0.00022 | $0.00242 |
| Sonnet 5.5 | $0.00011 | $0.00121 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
verification 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 1mo 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 — 51 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Verification
Turn the task's acceptance criteria into trustworthy evidence. Verification is part of implementation, not a ceremonial final command.
Build an evidence matrix
For each requirement, identify:
- the observable behavior or invariant;
- the cheapest deterministic check that can falsify it;
- the artifact that records the result (test report, exit code, diff, event ledger, benchmark, or trace);
- gaps that require a broader integration test or human decision.
Use existing repository commands and focused tests first. Add a regression test when the behavior was previously broken or the boundary is subtle.
Derive semantic boundary cases
Turn every normative sentence in the requirement and relevant repository contract into a falsifiable example before accepting a visible green test.
- For parsers and serializers, preserve delimiter and escape provenance until structural tokenization is complete. Test adjacent escapes, escaped delimiters, whitespace normalization, malformed escapes, exact field counts, duplicates, empty values, and round trips where applicable.
- For idempotent or batched mutations, validate both shape and semantic conflicts across the entire batch and against existing state before the first write. Test same-key/same-payload and same-key/different-payload behavior, serial and concurrent calls, and assert rejected operations leave state unchanged.
- For state machines, test invalid transitions, retries, stale revisions, cancellation, and crash/restart windows rather than only the happy path.
- For resource limits, test the exact boundary, one below, one above, and cumulative accounting after retry or restart.
Interpret results correctly
- A successful compile does not prove runtime behavior.
- A test command that selects zero tests is not a pass.
- A skipped environment-dependent security test must be identified and covered elsewhere when it matters.
- Tool text beginning with an error, a non-zero exit, timeout, cancellation, incomplete assertion set, or missing artifact cannot count as success evidence.
- Validate streamed/durable systems from committed state and replay, not only the live UI.
- For concurrency and recovery, exercise the race/crash boundary rather than infer correctness from sequential code.
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.
- 1mo ago First seen · 51 lines · 54 tokens per session scan A 1c7935dadc3d
verification is a skill published in the GitHub repository ageerle/ruoyi-ai (5,773 stars, last pushed 2d ago), licensed MIT. It adds 54 tokens to every session and 604 once invoked, about $0.0002 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 2026-08-30.
Other skills, from other repositories
fix-failing-tests
Diagnose a failing test in the googleapis/mcp-toolbox repo and land a fix by reasoning from the actual error: read the failure, reproduce it, shrink it until the cause is forced into the open, then fix the cause. Use this whenever a test or CI job is red, a build breaks after a change, many packages fail at once, or a…
reproduce-bug
Reproduce a reported bug in googleapis/mcp-toolbox and decide whether it is real, delivering an evidence-backed verdict: confirmed, already fixed, misconfiguration, client-side, works as intended, not reproducible, or blocked. Use whenever a maintainer asks you to reproduce, verify, confirm, or investigate a bug…
dev-browser
Browser automation with persistent page state. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website"…
potpie-debug-memory
Use while debugging or troubleshooting failures, flaky tests, incidents, production alerts, CI failures, local dev setup issues, repeated bugs, prior fixes, failed attempts, and verification history.
langsmith-observability
LLM observability platform for tracing, evaluation, and monitoring. Use when debugging LLM applications, evaluating model outputs against datasets, monitoring production systems, or building systematic testing pipelines for AI applications.
browserwing-admin
Manage and operate BrowserWing — an intelligent browser automation platform. Install dependencies, configure LLM, create/manage/execute automation scripts, use AI-driven exploration to generate scripts, browse the script marketplace, and troubleshoot issues.