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computer-task-execution计算机任务执行

Agent Skill

computer-task-execution 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,775

周安装

118

GitHub Stars

公开资料未说明

下载量

972
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:computer-task-execution(计算机任务执行)
来源仓库:https://github.com/gift-is-coding/computer-task-execution
安装命令:
openclaw skills install computer-task-execution
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install computer-task-execution

简介

使用具有验证和学习模式的最可靠、侵入性最小的方法在网站或本地应用程序上执行多步骤用户任务。

SKILL.md

name
computer-task-execution
description
Execute real user tasks across websites and local applications with a reliability-first strategy. Use when the user wants the agent to actually do something, not just answer: operate a website, complete a flow in a desktop app, send a message, edit content in a GUI, retrieve information from an app, or finish a multi-step computer task. Especially use when deciding between browser execution, app scripting, local UI automation, or low-intrusion execution paths.

computer-task-execution

Core idea

Execute the task, not the interface.

Start from the user goal and choose the path with the best combination of:

  • success rate
  • verifiability
  • low user disruption
  • low repeated trial-and-error

Do not default to local GUI automation. Prefer interfaces that are easier to verify and less intrusive.

Golden rules

  1. Prefer reliability over cleverness. A short visible foreground step is better than an invisible but flaky flow.
  2. Prefer data/interface layers over GUI simulation. If an API, scriptable interface, URL scheme, local file, browser DOM, or official automation surface exists, use that before UI driving.
  3. Prefer browser execution over local-app execution when the same task can be done on the web with adequate login state and verification.
  4. Treat verification as part of execution. A task is not done until the result is checked using the most reliable available evidence.
  5. Minimize focus stealing, but do not worship zero-focus-steal. The correct target is minimum user disruption with high confidence, not purity.
  6. Reuse proven patterns. Once a path has succeeded for a software target or domain, record it and use it first next time.

Decision model

Before acting, classify the task:

A. Read-only task

Examples:

  • read meeting details
  • inspect app state
  • fetch notifications
  • extract page content
  • check whether something exists

Default preference:

  1. browser/web task path
  2. local files / local database / app-exported data
  3. official scripting interface
  4. local app UI automation

B. Write-without-send task

Examples:

  • create a draft
  • fill a form but do not submit yet
  • edit a spreadsheet
  • prepare a document
  • populate a field

Default preference:

  1. browser/web task path
  2. official scripting / URL scheme / file-based write
  3. local app UI automation with minimum foreground time

C. High-risk action task

Examples:

  • send a message
  • submit a form
  • delete or modify live data
  • post to social media
  • trigger an approval flow

Default preference:

  1. browser/web task path if reliable and verifiable
  2. official app interface if supported and verifiable
  3. local app UI automation with explicit pre-check and post-check

For high-risk actions, if correct targeting depends on visible UI state, expect foreground execution for the critical step.

Execution-path priority

Always try paths in this order unless the task or accumulated target knowledge strongly suggests otherwise:

  1. Browser/web execution

- Best when the target service is available on the web - Prefer DOM-visible, page-verifiable flows - Best default for logged-in services when browser access is available

  1. Official non-UI interface

- App scripting - URL schemes - built-in automation hooks - import/export surfaces - local data files or supported storage

  1. Hybrid execution

- Prepare in background using data or browser - Switch to foreground only for the minimum critical action window - Immediately verify and exit

  1. Local app UI automation

- Use only when the task cannot be completed more reliably elsewhere - Prefer keyboard-first flows only when target focus can be guaranteed - Use visual or state verification for completion

  1. Background/local no-focus experimentation

- Last resort for low-risk tasks or explicit user request - Treat as experimental unless already proven for this target - If success is not strongly verifiable, do not present it as complete

Focus policy

When background/no-focus execution is appropriate

Prefer trying no-focus or low-focus execution when:

  • the task is read-only
  • the action operates on data rather than UI state
  • the target exposes a scriptable surface
  • verification does not depend on visible window state
  • the user explicitly requests silent/minimal-disruption mode

When foreground execution is usually necessary

Use foreground execution for the critical step when:

  • keyboard input must reach a specific target window or field
  • the result depends on visible UI state
  • recipient/target selection must be visually confirmed
  • the task sends, submits, deletes, or approves something
  • previous background attempts for this target were flaky

Preferred compromise

For local app tasks, default to:

  1. background preparation
  2. shortest possible foreground critical section
  3. post-action verification
  4. return control promptly

This is usually better than forcing a fully background flow.

Verification rules

Use the strongest available verification method:

  1. target-system confirmation

- message appears in thread - record exists - page shows success state - saved content is readable from source

  1. direct state read-back

- reread the object after write - check the updated field value - reload and confirm persistence

  1. visual confirmation

- screenshot or visible state inspection - only acceptable when stronger read-back is unavailable

  1. process-only confirmation

- use only for low-risk tasks - “command ran” is not sufficient evidence for a high-risk task

If you cannot verify confidently, say so and keep the result provisional.

Pattern memory: learn per software target

After each successful or meaningfully failed execution, update target-specific experience. If a relevant pattern already exists, reading it before execution is mandatory.

Storage

  • Website/domain patterns: references/site-patterns/<domain>.md
  • Local app patterns: references/site-patterns/<app-name>.md

What to record

Record only facts learned through execution:

  • successful path
  • required preconditions
  • whether foreground was necessary
  • whether browser was superior to app
  • known unstable paths
  • verification method that worked
  • date of discovery

Why this matters

Next time, if the target matches a known app or domain:

  1. read that pattern file first
  2. reuse the proven path first
  3. skip previously disproven paths unless the environment changed

This avoids repeated “try everything again” behavior.

Pattern file format

---
kind: local-app | website
name: WeChat
domain: x.com
app_id: com.tencent.xinWeChat
aliases: [微信, WeChat]
updated: 2026-03-27
---

## Successful paths
- [2026-03-27] Foreground: open -a WeChat → Command+F → paste contact → Enter → paste message → Enter

## Preconditions
- Main window present
- Search accepts pasted contact names

## Verification
- Sent message visibly appears in the target thread

## Unstable or failed paths
- [2026-03-27] Background-only keyboard injection was not reliably targetable

## Recommended default
- Use background preparation + short foreground execution + post-send verification

How to use pattern memory

Step 1: identify the target

Normalize the target to either:

  • a domain, or
  • an application name

Step 2: check for prior knowledge

If a matching pattern file exists, you must read it before choosing the execution path. Do not skip this step just because you already have a generic plan.

Step 3: start with the proven route

If the stored preferred route still fits the current request, use it first. Treat stored successful patterns as the default starting point.

Step 4: only explore when needed

Explore alternatives only if:

  • the preferred route fails
  • the user requested a different mode
  • the environment clearly changed
  • the stored pattern is clearly inapplicable to the current task

Do not re-run previously disproven paths unless there is a specific reason.

Step 5: update after execution

If new facts were learned, update the pattern file. Pattern memory is part of task completion, not optional cleanup.

Choosing browser vs local app

Prefer browser when

  • the service has a working web app
  • login state exists in browser
  • DOM/state inspection improves confidence
  • you need robust, repeatable verification
  • avoiding frontmost app disruption matters

Prefer local app when

  • the task is app-only
  • the app exposes better native automation than the website
  • the browser version is missing key capabilities
  • the task is already known to work reliably through a stored app pattern

Local app operating style

If local app execution is necessary, prefer this sequence:

  1. Determine exact success criteria
  2. Prepare all inputs before touching the app
  3. Open or locate the target app/window
  4. Keep foreground time as short as possible
  5. Execute only the critical path
  6. Verify immediately
  7. Record the winning pattern

Handling silent mode requests

If the user asks to avoid stealing focus:

  • first see whether browser or non-UI paths can satisfy the task
  • if local-app background execution is only partially reliable, say so internally in planning and choose it only when the task risk is low or the user explicitly prefers silence over certainty
  • for high-risk tasks, recommend minimum-foreground execution rather than pretending a background path is equally safe

Failure handling

When a path fails:

  1. identify whether the failure was due to targeting, focus, auth, UI drift, missing permissions, or bad path choice
  2. switch to the next-best path class instead of repeating the same failing method blindly
  3. if the failure teaches something reusable, record it in the target pattern file

References

  • Read references/pattern-memory.md for the pattern-memory policy.
  • Read references/site-patterns/<target>.md when a known software target or domain already has stored experience.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

91.01%
按下载量换算885

安全审计

VirusTotal

未展示

ClawScan

可疑

Static analysis

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

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来源信息

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