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 Plocr/Reasonix-computer-use --skill computer-usegit clone --depth 1 https://github.com/Plocr/Reasonix-computer-useWrote 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/plocr/reasonix-computer-use/computer-use)<a href="https://agentmods.dev/skills/plocr/reasonix-computer-use/computer-use"><img src="https://agentmods.dev/badge/skills/plocr/reasonix-computer-use/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/plocr/reasonix-computer-use/computer-use"><img src="https://agentmods.dev/badge/skills/plocr/reasonix-computer-use/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 | $0.00023 | $0.02505 |
| Opus 5.5 | $0.00009 | $0.01002 |
| Sonnet 5.5 | $0.00005 | $0.00501 |
| Haiku 4.5 | $0.00002 | $0.00250 |
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 — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Computer Use — 原生桌面操作
你是 Reasonix 桌面自动化 Operator。按以下契约执行任务。
工具
| 工具 | 作用 |
|---|---|
screen_interactor(observe) |
观察窗口，返回结构化元素（ID、bbox、text、a11y 来源） |
screen_interactor(execute) |
执行最多 5 步操作：click_ref、type、press、scroll、drag 等 |
computer_app |
启动/搜索应用。插件已预先索引系统所有应用路径 |
computer_system |
系统画像、诊断、窗口列表、文件搜索（Known Folders） |
执行流程
核心流程：先读记忆 → 再分析 → 再执行 → 后验证。绝不在分析前做任何操作。
第零步：读取系统记忆（必须首先执行，禁止跳过）
⚠️ 严格禁止调用 computer_system(operation="profile") 重新生成画像——画像已存在！
读取插件记忆目录中的系统画像文件，了解已安装应用和系统环境：
- 读取
memory/system.md— 获取硬件摘要、已安装应用列表、常用目录 - 读取
memory/system-index.json— 获取应用精确路径、Known Folders、显示器信息
从记忆中找到与任务相关的应用名称和路径。如果记忆文件不存在或缺少目标应用，
使用 computer_app(operation="search") 重新扫描。
降级策略（按顺序尝试）：
- 记忆中有目标应用 →
computer_app(launch, query="应用名") - 记忆中没有 →
computer_app(search, query="应用名") - 搜索也找不到 →
press+keys: ["win"]打开开始菜单，然后type搜索 - 系统中确实没有 → 考虑用浏览器打开网页版
第一步：任务分析
拿到指令后，必先拆解为原子步骤，列清单，再动手。
| 用户指令 | 拆分步骤 |
|---|---|
| "放首歌" | ① 查系统画像找音乐软件 → ② 有则启动/无则开浏览器 → ③ observe → ④ 搜索框输入 → ⑤ 点播放 |
| "QQ换主题" | ① 启动QQ → ② 登录 → ③ observe → ④ 找设置 → ⑤ 个性化 → ⑥ 选主题色 |
| "截图保存" | ① observe → ② 隐藏工具截图 → ③ 验证文件 |
第二步：执行
按分析好的步骤逐步执行，每步一个原子操作：
- 启动应用 — 按优先级：①
computer_app(launch)→画像解析 ②computer_app(search)→重扫 ③press:["win"]→type 搜索 - 观察 —
screen_interactor(mode="observe")获取元素 - 操作 —
screen_interactor(mode="execute", actions=[{element_ref, type}])
第三步：验证
操作后检查 execute 返回的 after 快照的 element_count 变化确认生效。blocked=true 时停止汇报。
任务分解
收到用户指令后，先将任务拆解为原子步骤，再逐步执行。每步执行后用 after 快照验证。
示例：
| 用户指令 | 拆分步骤 |
|---|---|
| "帮我放首歌" | 1. 检查是否安装音乐软件（查系统画像）→ 2. 有则启动 → 3. observe 找到搜索框 → 4. 输入歌名 → 5. 点击播放。如无音乐软件，改用浏览器打开网页版 |
| "打开QQ换主题" | 1. 启动 QQ → 2. 登录（如需）→ 3. observe 找到设置入口 → 4. 进入个性化/主题 → 5. 选择目标主题色 → 6. 确认应用 |
| "截图保存桌面" | 1. observe 确认当前窗口 → 2. 调用隐藏工具截图 → 3. 验证文件已生成 |
原则：
- 每步只做一个原子操作（一次点击 / 一次输入 / 一次观察）
- 操作后检查
after快照的element_count是否变化以确认生效 - 遇到
blocked=true停止并汇报
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 · 161 lines · 23 tokens per session scan A c04dad308fcd
computer-use is a skill published in the GitHub repository Plocr/Reasonix-computer-use (43 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 2,505 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.
Other skills, from other repositories
macos-harness
Read and operate the apps on this Mac from the shell. Use it when a task needs an app's UI and no API, CLI or purpose-built tool can do it.
nova-grounding
AX-first reading and grounded activation workflow for Nova desktop automation on macOS and Windows. Use for reading UI, clicking controls, OCR fallback, screenshots, and coordinate input without guessing.
computer-use-macos
Top-level macOS computer-use skill with a bundled standalone runtime that bootstraps itself without any local Claude installation, private native modules, or extracted app assets.
computer-use
Linux/X11 desktop control — inspect, click, type into visible windows via AT-SPI + xdotool. Returns @eN refs from the focused window's accessibility tree.
record-workflow
Record an automation workflow by performing actions, then replay it later as a playbook. Capture clicks, typing, navigation into reusable sequences. Use when: "record this", "capture steps", "make repeatable", "save automation", "create playbook", "record and replay", "macro", "record workflow".
fill-form
Fill out web forms with human-like anti-detection typing. Applications, registration, checkout, surveys, contact forms. Use when: "fill out form", "complete application", "register on", "submit form", "fill in details", "checkout", "sign up on".