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 latestaiagents/agent-skills --skill computer-usegit clone --depth 1 https://github.com/latestaiagents/agent-skillsWrote 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/latestaiagents/agent-skills/computer-use)<a href="https://agentmods.dev/skills/latestaiagents/agent-skills/computer-use"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/computer-use/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/latestaiagents/agent-skills/computer-use"><img src="https://agentmods.dev/badge/skills/latestaiagents/agent-skills/computer-use.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 | $0.00100 | $0.01480 |
| Opus 5.5 | $0.00040 | $0.00592 |
| Sonnet 5.5 | $0.00020 | $0.00296 |
| Haiku 4.5 | $0.00010 | $0.00148 |
Grade A, and why
computer-use 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 — 153 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Use
Computer Use lets Claude control a virtual computer — take screenshots, move cursor, click, type. Use it when there's no API, not as a first resort.
When to Use
- Automating legacy apps with no API
- Cross-app workflows (copy from A, paste to B, click approve)
- QA/UI testing where you want the agent to drive a real browser
- End-to-end automation mixing web and desktop
When NOT to Use
- Anything that has an API — APIs are 10-100× faster and cheaper
- High-frequency tasks — each action is a model call (slow)
- Anything where a failed click has real consequences (payments, deletes) without human in the loop
- Untrusted environments — the agent can be injected via screen content
Tool Shape
const tools = [
{ type: "computer_20250124", name: "computer", display_width_px: 1280, display_height_px: 800, display_number: 1 },
{ type: "text_editor_20250124", name: "str_replace_editor" }, // optional
{ type: "bash_20250124", name: "bash" }, // optional
];
const response = await client.beta.messages.create(
{
model: "claude-sonnet-4-6",
max_tokens: 4096,
tools,
messages: [{ role: "user", content: "Open the browser and find Claude's API pricing page." }],
},
{ headers: { "anthropic-beta": "computer-use-2025-01-24" } },
);
The model returns tool_use blocks with actions like screenshot, left_click, type, key, mouse_move, scroll.
Reference Implementation
Anthropic ships a reference Docker container (ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest) with:
- Virtual X server (Xvfb)
- Firefox + common apps
- VNC for human observation
- HTTP shim that executes tool calls
Don't run this on a host with real user data. It's a demo. For production, use a sandboxed cloud VM (Firecracker, Vercel Sandbox, cloud-hypervisor).
Action Loop
async function run(goal: string) {
const messages = [{ role: "user", content: goal }];
while (true) {
const response = await client.beta.messages.create({
model: "claude-sonnet-4-6",
max_tokens: 4096,
tools,
messages,
}, { headers: { "anthropic-beta": "computer-use-2025-01-24" } });
messages.push({ role: "assistant", content: response.content });
if (response.stop_reason !== "tool_use") return response;
const results = [];
for (const block of response.content) {
if (block.type === "tool_use") {
const output = await executeAction(block.name, block.input); // your VM controller
results.push({
type: "tool_result",
tool_use_id: block.id,
content: output.screenshot ? [{ type: "image", source: output.screenshot }] : output.text,
});
}
}
messages.push({ role: "user", content: results });
}
}
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 · 153 lines · 100 tokens per session scan A 40e8001286c6
computer-use is a skill published in the GitHub repository latestaiagents/agent-skills (6 stars, last pushed 6mo ago), licensed MIT. It adds 100 tokens to every session and 1,480 once invoked, about $0.0004 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 2026-09-03.
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