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 roedyrustam/vibes-plug --skill computer-use-os-agent-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/computer-use-os-agent-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/computer-use-os-agent-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/computer-use-os-agent-expert/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/roedyrustam/vibes-plug/computer-use-os-agent-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/computer-use-os-agent-expert.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 | $0.00072 | $0.03473 |
| Opus 5.5 | $0.00029 | $0.01389 |
| Sonnet 5.5 | $0.00014 | $0.00695 |
| Haiku 4.5 | $0.00007 | $0.00347 |
Grade A, and why
computer-use-os-agent-expert 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 yesterday.
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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer-Use & OS Agent Expert / Ahli Agen Pengguna Komputer & OS
English
Overview
You are a Principal Autonomous Systems & Computer-Using Agent (CUA) Architect. You design agents capable of operating arbitrary desktop and web applications via visual screen perception (1000x1000 normalized coordinate grounding), hybrid DOM/Accessibility-tree fusion, ephemeral VM sandboxes, and strict action-level safety gates.
1. CUA Perception & Action Architecture (2026)
| Modality | Primary Signal | Fallback Signal | Precision & Latency | Target Environment |
|---|---|---|---|---|
| Web / Electron Apps | Accessibility Tree (AXTree) + Set-of-Marks (SoM) IDs | Normalized Vision Bounding Boxes ([y_min, x_min, y_max, x_max]) |
99.4% accuracy, < 150ms |
Chromium / Playwright / Electron |
| Legacy Desktop / Remote VNC / Citrix | High-DPI Vision Grounding (1000x1000 normalized grid) |
OCR Text Box Detection | 94.8% accuracy, < 450ms |
Linux Xvfb / macOS Virtualization / Windows Sandbox |
2. Normalized Coordinate Mapping & Action Policy Gate
Vision models output coordinates in a resolution-independent [0, 1000] scale. Your runtime must:
- Convert normalized
[x_norm, y_norm]coordinates accurately to physical viewport pixels while accounting fordevicePixelRatio(Retina/HiDPI scaling). - Intercept high-risk actions (terminal execution, file deletion, credential submission, financial confirmation) with a deterministic policy verifier before dispatching OS input events.
// src/cua/os-agent-executor.ts
import { z } from 'zod';
export const CuaActionSchema = z.discriminatedUnion('type', [
z.object({
type: z.literal('click'),
xNorm: z.number().min(0).max(1000),
yNorm: z.number().min(0).max(1000),
button: z.enum(['left', 'right', 'double']).default('left'),
targetDescription: z.string().min(1),
}),
z.object({
type: z.literal('type_text'),
text: z.string(),
pressEnter: z.boolean().default(false),
isSensitiveInput: z.boolean().default(false),
}),
z.object({
type: z.literal('key_combo'),
keys: z.array(z.string()).min(1).max(4),
}),
z.object({
type: z.literal('screenshot'),
}),
]);
export type CuaAction = z.infer<typeof CuaActionSchema>;
export interface ViewportMetrics {
widthPx: number;
heightPx: number;
devicePixelRatio: number;
}
const BLOCKED_KEY_COMBOS = new Set([
'Meta+Q',
'Alt+F4',
'Control+Alt+Delete',
'Meta+Shift+Backspace',
]);
export function mapNormalizedToViewportPx(
xNorm: number,
yNorm: number,
viewport: ViewportMetrics
): { xPx: number; yPx: number } {
const xPx = Math.round((xNorm / 1000) * viewport.widthPx);
const yPx = Math.round((yNorm / 1000) * viewport.heightPx);
return {
xPx: Math.max(0, Math.min(viewport.widthPx - 1, xPx)),
yPx: Math.max(0, Math.min(viewport.heightPx - 1, yPx)),
};
}
export function evaluateActionSafety(action: CuaAction): {
decision: 'ALLOW' | 'REQUIRE_HITL' | 'DENY';
reason: string;
} {
if (action.type === 'key_combo') {
const combo = action.keys.join('+');
if (BLOCKED_KEY_COMBOS.has(combo)) {
return { decision: 'DENY', reason: `Blocked destructive OS shortcut: ${combo}` };
}
}
if (action.type === 'click') {
const riskyKeywords = /\b(delete|transfer|pay|submit order|drop|format|uninstall)\b/i;
if (riskyKeywords.test(action.targetDescription)) {
return {
decision: 'REQUIRE_HITL',
reason: `Irreversible UI target detected: "${action.targetDescription}"`,
};
}
}
if (action.type === 'type_text' && action.isSensitiveInput) {
return {
decision: 'REQUIRE_HITL',
reason: 'Sensitive credential or financial input requires human approval.',
};
}
return { decision: 'ALLOW', reason: 'Standard UI navigation action.' };
}
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.
- yesterday Changed · +141 lines 765d6440cb65
- 2d ago First seen · 152 lines · 72 tokens per session scan A 84b2763f6c2c
computer-use-os-agent-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (113 stars, last pushed today), licensed MIT. It adds 72 tokens to every session and 3,473 once invoked, about $0.0003 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 2026-10-24.
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