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 humanerd-drew/opencode-drewgent --skill macos-computer-usegit clone --depth 1 https://github.com/humanerd-drew/opencode-drewgentWrote 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/humanerd-drew/opencode-drewgent/macos-computer-use)<a href="https://agentmods.dev/skills/humanerd-drew/opencode-drewgent/macos-computer-use"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/macos-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/humanerd-drew/opencode-drewgent/macos-computer-use"><img src="https://agentmods.dev/badge/skills/humanerd-drew/opencode-drewgent/macos-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 | $0.00060 | $0.01865 |
| Opus 5.5 | $0.00024 | $0.00746 |
| Sonnet 5.5 | $0.00012 | $0.00373 |
| Haiku 4.5 | $0.00006 | $0.00186 |
Grade A, and why
macos-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.
This is a copy
89% identical to macos-computer-use — 406 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
macOS Computer Use (universal, any-model)
You have a computer_use tool that drives the Mac in the background.
Your actions do NOT move the user's cursor, steal keyboard focus, or switch
Spaces. The user can keep typing in their editor while you click around in
Safari in another Space. This is the opposite of pyautogui-style automation.
Everything here works with any tool-capable model — Claude, GPT, Gemini, or an open model running through a local OpenAI-compatible endpoint. There is no Anthropic-native schema to learn.
The canonical workflow
Step 1 — Capture first. Almost every task starts with:
computer_use(action="capture", mode="som", app="Safari")
Returns a screenshot with numbered overlays on every interactable element AND an AX-tree index like:
#1 AXButton 'Back' @ (12, 80, 28, 28) [Safari]
#2 AXTextField 'Address and Search' @ (80, 80, 900, 32) [Safari]
#7 AXLink 'Sign In' @ (900, 420, 80, 24) [Safari]
...
Step 2 — Click by element index. This is the single most important habit:
computer_use(action="click", element=7)
Much more reliable than pixel coordinates for every model. Claude was trained on both; other models are often only reliable with indices.
Step 3 — Verify. After any state-changing action, re-capture. You can save a round-trip by asking for the post-action capture inline:
computer_use(action="click", element=7, capture_after=True)
Capture modes
mode |
Returns | Best for |
|---|---|---|
som (default) |
Screenshot + numbered overlays + AX index | Vision models; preferred default |
vision |
Plain screenshot | When SOM overlay interferes with what you want to verify |
ax |
AX tree only, no image | Text-only models, or when you don't need to see pixels |
Actions
capture mode=som|vision|ax app=… (default: current app)
click element=N OR coordinate=[x, y]
double_click element=N OR coordinate=[x, y]
right_click element=N OR coordinate=[x, y]
middle_click element=N OR coordinate=[x, y]
drag from_element=N, to_element=M (or from/to_coordinate)
scroll direction=up|down|left|right amount=3 (ticks)
type text="…"
key keys="cmd+s" | "return" | "escape" | "ctrl+alt+t"
wait seconds=0.5
list_apps
focus_app app="Safari" raise_window=false (default: don't raise)
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 · 206 lines · 60 tokens per session scan F 2ee295ec65ed
macos-computer-use is a skill published in the GitHub repository humanerd-drew/opencode-drewgent (3 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 1,865 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to macos-computer-use, differing in 406 lines, and is treated as a copy.
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