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 zavora-ai/computer-use-mcp --skill computer-usegit clone --depth 1 https://github.com/zavora-ai/computer-use-mcpWrote 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/zavora-ai/computer-use-mcp/computer-use)<a href="https://agentmods.dev/skills/zavora-ai/computer-use-mcp/computer-use"><img src="https://agentmods.dev/badge/skills/zavora-ai/computer-use-mcp/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/zavora-ai/computer-use-mcp/computer-use"><img src="https://agentmods.dev/badge/skills/zavora-ai/computer-use-mcp/computer-use.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 | $0.00037 | $0.01195 |
| Opus 5.5 | $0.00015 | $0.00478 |
| Sonnet 5.5 | $0.00007 | $0.00239 |
| Haiku 4.5 | $0.00004 | $0.00120 |
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 today.
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 — 54 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Use (desktop automation)
When to use
- Native desktop apps, installers, modal dialogs, simulators
- Apps that draw their own UI (Unreal, Blender, games): OCR route below
- UI-only workflows with no API/CLI
When NOT to use
- Prefer the app's own API or MCP server, connectors, shell, filesystem, or browser automation (Playwright) first
Mandatory first steps
If the app ID is unknown, first use discover_applications({query, include_capabilities: true}). Use its targetApp when present; do not assume a desktop registration ID is a process target.
get_tool_guide({ task_description })— pick scripting vs accessibility vs OCR vs coordinatesget_app_capabilities({ bundle_id })— is the app scriptable?accessibility: { nodes, hasControls }says whether its tree has real controls (accessiblemeanshasControls); a handful of nodes means it draws its own UI- Route order:
run_script→ accessibility (fill_form,click_element) → OCR (read_window_text,click_text) → coordinates (click,type,key) last
Route for apps that draw their own UI (macOS)
Unreal, Blender, games and Electron canvases expose an empty accessibility tree. Do not call get_ui_tree, find_element or list_menu_bar on them.
list_windows(kinds:main,document,dialog,panel,toast) to pick the windowread_window_textwith aregion(keeps it to a few dozen tokens): lines withboxin window points; screen point =screen_origin + box. On-device, works on covered windows, activates nothingclick_textto press a label by what it says (match:exact|contains|regex,nth);wait_for_text(appears, orgone: true) orwait_for_stableafter the click instead of sleepingscreenshot/zoomonly when text is not enough (icons, 3D viewports);mouse_dragfor orbit/pan These OCR tools exist on macOS only; on Windows and Linux they are not listed.
Hard rules
- Always set
target_app(bundle ID macOS / process name Windows) ortarget_window_id— or callset_targetonce and omit it afterwards (get_targetshows it; an explicit target on a call wins) - One
click {coordinate, button: left|right|middle, count: 1|2|3};left_click,double_clickand the rest are aliases of it - Do not screenshot every step: use
get_ui_tree/find_elementwhere the app has accessibility,read_window_textwhere it does not;screenshotwith a target captures that window (full_screen: truefor the whole screen) - Wait with
wait_for_window,wait_for_text,wait_for_stable, notwaitplus a screenshot loop - Long text:
write_clipboard+ paste shortcut, nottype - On focus failure, follow
suggestedRecovery(activate_window,unhide_app,open_application) - Use
focus_strategy: "prepare_display"only after a focus race
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.
- today Changed 5e567e994349
- 12d ago Changed 512d1795c725
- 15d ago Changed · +23 lines · +1 tokens per session 13a6e3a86e74
- 1mo ago Changed · +1 lines ecbab29544c7
- 1mo ago First seen · 30 lines · 36 tokens per session scan A 4228d8fa8a4f
computer-use is a skill published in the GitHub repository zavora-ai/computer-use-mcp (86 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 1,195 once invoked, about $0.0001 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.
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