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Computer Task Execution

作者 gift-is-coding · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install computer-task-execution
功能描述
Execute multi-step user tasks on websites or local apps using the most reliable, minimally intrusive method with verification and learned patterns.
使用说明 (SKILL.md)

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
  2. Official non-UI interface

    • App scripting
    • URL schemes
    • built-in automation hooks
    • import/export surfaces
    • local data files or supported storage
  3. Hybrid execution

    • Prepare in background using data or browser
    • Switch to foreground only for the minimum critical action window
    • Immediately verify and exit
  4. 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
  5. 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
  2. direct state read-back

    • reread the object after write
    • check the updated field value
    • reload and confirm persistence
  3. visual confirmation

    • screenshot or visible state inspection
    • only acceptable when stronger read-back is unavailable
  4. 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/\x3Cdomain>.md
  • Local app patterns: references/site-patterns/\x3Capp-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/\x3Ctarget>.md when a known software target or domain already has stored experience.
安全使用建议
This skill is coherent as a playbook for automating tasks, but it assumes access to local/browser sessions and persistent storage for per-site patterns without declaring where those files live or what they may contain. Before installing: (1) confirm how and where pattern memory will be stored and who can read it, (2) restrict or review any platform permissions that let the agent control the GUI/clipboard/screenshots, (3) avoid leaving sensitive accounts logged into browsers or apps you don't want automated, (4) prefer explicit user confirmation for high-risk actions (sending messages, deleting data), and (5) ask the developer to document exact runtime capabilities and storage behavior. If you cannot get those clarifications, treat the skill as higher risk and avoid enabling autonomous invocation or long-lived persistence.
能力评估
Purpose & Capability
The name/description describe executing real user tasks and the SKILL.md provides a coherent decision model for doing that. However, the instructions assume capabilities (local GUI automation, browser DOM inspection, screenshots, and persistent 'pattern memory' updates) that are not declared in the skill metadata (no config paths, no required binaries). That omission is plausible for an instruction-only skill, but it's a meaningful gap between claimed requirements and implied actions.
Instruction Scope
Instructions direct runtime behavior that touches sensitive surfaces: driving local apps, focusing windows, pasting via clipboard, reading DOM/browser state, taking screenshots/visual verification, and updating/reusing a persistent pattern memory. Those actions can access personal data (messages, documents, calendar items). The skill does not explicitly limit what is recorded in pattern memory or where it is stored, and it gives broad discretion ('choose the most reliable method'), which could lead to unexpected data reads or writes.
Install Mechanism
This is instruction-only and has no install spec or external downloads. That minimizes supply-chain risk because no code is pulled during install.
Credentials
The skill requests no environment variables or credentials in metadata, which is proportionate. But it repeatedly assumes browser login state and access to local app accounts; it also expects to persist target-specific patterns. The absence of declared storage/config paths or explicit permission prompts is a gap worth clarifying.
Persistence & Privilege
SKILL.md mandates updating 'pattern memory' after runs (read-before-run is mandatory if a pattern exists). The skill package includes pattern templates, but runtime writes/reads to persistent storage are implied without any declared config path or explanation of storage location, retention, or sensitivity controls. Combined with autonomous invocation (allowed by default), this persistent behavior increases risk if not constrained.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install computer-task-execution
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /computer-task-execution 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
computer-task-execution 1.0.0 initial release - Launches a new skill focused on reliably executing user tasks across websites and local apps. - Establishes a reliability-first decision model for choosing execution paths: browser/web, official interfaces, hybrid, UI automation, and background. - Provides golden rules for minimizing user disruption and improving success verification. - Introduces pattern memory to store and reuse per-target execution knowledge. - Details verification standards and a format for documenting successful and failed patterns.
元数据
Slug computer-task-execution
版本 1.0.0
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 1
常见问题

Computer Task Execution 是什么?

Execute multi-step user tasks on websites or local apps using the most reliable, minimally intrusive method with verification and learned patterns. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 95 次。

如何安装 Computer Task Execution?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install computer-task-execution」即可一键安装,无需额外配置。

Computer Task Execution 是免费的吗?

是的,Computer Task Execution 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Computer Task Execution 支持哪些平台?

Computer Task Execution 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。

谁开发了 Computer Task Execution?

由 gift-is-coding(@gift-is-coding)开发并维护,当前版本 v1.0.0。

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