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 agentmods add skills/thewolffish/wolffish-app/computer-usenpx skills add thewolffish/wolffish-app --skill computer-usegit clone --depth 1 https://github.com/thewolffish/wolffish-appWrote 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/thewolffish/wolffish-app/computer-use)<a href="https://agentmods.dev/skills/thewolffish/wolffish-app/computer-use"><img src="https://agentmods.dev/badge/skills/thewolffish/wolffish-app/computer-use.svg" alt="Measured on agentmods" 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 | $0.00053 | $0.06592 |
| Opus 5 | $0.00026 | $0.03296 |
| Sonnet 5 | $0.00011 | $0.01318 |
| Haiku 4.5 | $0.00005 | $0.00659 |
Grade C, and why
computer-use scanned grade C with 1 finding 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.
Recursive force deletehighDestructive command
rm -rf with a variable or a broad path is one typo away from removing the wrong tree.
- pattern: 'computer_keyboard_type.*(sudo|rm -rf|password|secret|token)' How it starts
The opening of the file, as written. The whole thing — 475 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Use — Verified-Aim Desktop Automation
The agent sees and controls the desktop through a closed feedback loop designed for surgical accuracy with any vision-capable model:
- One coordinate space, owned by the plugin. Every image the tools return (screenshot, zoom, or click magnifier) becomes the current frame. The model always gives coordinates as pixels read off the latest image; the plugin does all translation to real screen position — screenshot downscaling, Retina/HiDPI scale factors, and multi-monitor offsets. The model never does coordinate math, which removes the entire class of "right target, wrong space" misses.
- A crosshair marks the cursor on every returned image, so the model always knows where the pointer actually is. In aiming frames (zooms and magnifiers) thin hairlines additionally run from the image edges through the exact cursor pixel — a fat ring can visually swallow a 16px control that is actually 15px away, while a hairline either passes through the target or visibly does not.
- Zoom for small targets.
computer_zoomre-captures a chosen region at native resolution (up to 4x magnification). The zoomed image becomes the frame, so tiny controls are clicked in a space where they are dozens of pixels wide. A zoom below 2x is flagged in the result, with the region size that would reach 3x — weak "zooms" of wide regions are where small-target clicks historically missed. - Every click returns proof — including an objective change report.
computer_mouse_clickcaptures the screen before and after the press and reports what percentage of pixels changed around the cursor and across the display. "NO visible change" after a click that should have had an immediate local effect (closed a tab, opened a menu) means the click missed, regardless of how the close-up reads. The check samples one display for under half a second, so slow-painting results, cross-display effects, and sub-threshold changes evade it — the model is told to verify with a screenshot before retrying, and never to blindly re-click side-effectful controls. Click, move, and drag also return a 3x-magnified close-up with the crosshair and hairlines on the exact pixel acted on, which becomes the current frame so corrections happen in finer coordinates instead of by re-guessing on the full screenshot.
This mirrors the practices Anthropic uses for Claude's own computer use: act on the latest image only, verify after every action, zoom rather than squint, prefer keyboard shortcuts when they are more reliable than pointing.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · +73 lines 1f5f907cd170
- 6d ago First seen · 402 lines · 53 tokens per session scan C efd9392c5fd6
computer-use is a skill published in the GitHub repository thewolffish/wolffish-app (5 stars, last pushed yesterday), licensed MIT. It adds 53 tokens to every session and 6,592 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 1 finding (recursive force delete). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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