universalbench — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited universalbench (Plugin) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
<p align="center"> <img src="./logo.svg" width="90" alt="UniversalBench" /> </p>
<h1 align="center">UniversalBench</h1>
<p align="center"> <strong>Your AI finally has hands.</strong> </p>
<p align="center"> Connect once. Your AI can now run code, push to GitHub, query databases,<br/> deploy, call APIs, manage credentials, and invoke any AI model.<br/> Not describe how. Actually do it. </p>
<p align="center"> <a href="https://universalbench.dev">universalbench.dev</a> · <a href="https://docs.universalbench.dev">Docs</a> · <a href="https://universalbench.dev/#signup">Sign up free</a> </p>
<p align="center"> <img src="https://img.shields.io/badge/free%20tier-1%2C000%20calls%2Fmonth-brightgreen" /> <img src="https://img.shields.io/badge/MCP-compatible-blue" /> <img src="https://img.shields.io/badge/works%20with-any%20AI-purple" /> </p>
Your AI is genuinely intelligent. It can reason, plan, write code, and solve complex problems.
But it cannot do anything in the real world on its own.
It can write a Python script. It cannot run it. It can suggest a Git commit. It cannot push it. It can write a database query. It cannot execute it. It can describe a deployment. It cannot ship it.
It knows how to do the work. It has no hands to do it with.
UniversalBench gives your AI a cloud computer it can use as a tool.
Connect one URL to any MCP-compatible AI. From that moment your AI can:
None of this is described. None of this is suggested. It is executed, confirmed, and returned.
1. Sign up free at universalbench.dev. No credit card. 1,000 free calls per month from day one.
2. Copy your API key from the dashboard.
3. Paste this URL into any AI client:
https://universalbench-mcp.penantiaglobal.workers.dev/u/YOUR_API_KEYSettings → Integrations → Add custom integration → paste the URL.
{
"mcpServers": {
"universalbench": {
"url": "https://universalbench-mcp.penantiaglobal.workers.dev/u/YOUR_API_KEY"
}
}
}{
"mcpServers": {
"universalbench": {
"command": "npx",
"args": ["-y", "@modelcontextprotocol/client-sse",
"https://universalbench-mcp.penantiaglobal.workers.dev/u/YOUR_API_KEY"]
}
}
}Works with any MCP-compatible client: Claude, ChatGPT, Gemini, GitHub Copilot, Cursor, Windsurf, and more.
Without UniversalBench, your AI writes the function, you copy it, run it yourself, see an error, paste it back. Back and forth until something works.
With UniversalBench, your AI writes the function, runs it, sees the error, fixes it, runs it again, confirms it works, pushes the commit, hits the health check, and tells you it is live. You asked once. It shipped.
You have a log file with 50,000 lines. Without a runtime, your AI reads as much as fits in context and estimates the rest.
With UniversalBench, your AI writes a script, runs it against the full file in the sandbox, and returns the exact answer. Every row processed. One round trip.
"Check the last 100 database rows for anomalies, open a GitHub issue if you find any, and send me a summary."
One prompt. Your AI queries the database, finds the anomaly, writes the GitHub issue, opens it, and responds with what it found. No copy-pasting. No manual steps. No back and forth.
"Run this summarisation task through three models and tell me which gives the best result per dollar."
Your AI calls each model, compares outputs, calculates cost per call, and reports back. UniversalBench enforces your spending ceiling before each call goes out.
Drop a messy spreadsheet into a link your AI gives you. It cleans it in the sandbox and hands back a polished file you can download. Ask it to check a page you just shipped, and it captures a full snapshot and returns a link that opens it. The files never pass through the chat, so there is no size limit and nothing to copy and paste.
These are not settings you configure. They run on every call, for every customer, by default.
Your AI cannot push code that breaks your service. Every push is validated before the commit lands. The previous version is captured automatically. Provide a health check URL and we run a smoke test after deploy. If it fails, the previous version is restored.
Your AI cannot burn your API budget. Every AI model call is cost-estimated before it runs. Calls over your ceiling are rejected. You set the limit. Default is $0.50 per call. Hard cap is $50.
Your AI cannot reach your internal network. Every outbound HTTP request is checked against a blocklist before it leaves. Private IP ranges, loopback, link-local, and cloud metadata endpoints are blocked at the infrastructure level.
One connection. Three tools (ub_read, ub_write, ub_ai). Underneath them, your AI can:
Every capability runs behind the same safety checks: validated before code lands, cost-estimated before a model runs, and network-checked before a request leaves.
Store a credential once and your AI uses it on every future call, without ever seeing the value. Encrypted at rest. Save once, and the relevant tools start working automatically.
| Free | 1,000 calls per month, no credit card |
| Pay as you go | $0.008 per call after the free tier |
| Minimum top-up | $5 |
No subscription. No lock-in. Credits do not expire.
Full capability reference, quickstart guides, and agent-ready prompts at docs.universalbench.dev.
<p align="center"> <a href="https://universalbench.dev/#signup"><strong>Sign up free, no credit card →</strong></a> </p>
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.