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@fzkuji/gui-agent

A

GUI automation via visual perception. Screenshot → detect → click → verify. Use when asked to operate a desktop app, interact with a VM, or complete an OSWorld benchmark task.

skillclaude

Install

agr install @fzkuji/gui-agent --target claude

Writes 7 files into .claude/skills/, pinned to git-45ba3f0a.

  • .claude/skills/gui-agent/.gitattributes
  • .claude/skills/gui-agent/.gitignore
  • .claude/skills/gui-agent/LICENSE
  • .claude/skills/gui-agent/README.md
  • .claude/skills/gui-agent/SKILL.md
  • .claude/skills/gui-agent/pyproject.toml
  • .claude/skills/gui-agent/requirements.txt

Document


name: gui-agent description: "GUI automation via visual perception. Screenshot → detect → click → verify. Use when asked to operate a desktop app, interact with a VM, or complete an OSWorld benchmark task."

GUI Agent

Autonomous GUI task execution. Give it a natural language task, it operates the desktop.

When to Use

Use gui-agent when the user asks you to:

  • Operate a desktop application (click buttons, fill forms, navigate menus)
  • Interact with a VM (OSWorld tasks)
  • Do anything that requires seeing and clicking on a screen

How to Use

gui-agent "your task description here"

Examples:

# Desktop automation
gui-agent "Open Firefox and go to google.com"
gui-agent "Send hello to John in WeChat"
gui-agent "Install the Orchis GNOME theme"

# Remote VM
gui-agent --vm http://172.16.82.132:5000 "Open GitHub in Chrome"

# With specific model
gui-agent --provider claude-code --model opus "Crop the top 20% of the image in GIMP"

Options

gui-agent [OPTIONS] TASK

TASK                  Natural language task description

--vm URL              Remote VM HTTP API
--provider NAME       Force LLM provider: claude-code, openclaw, anthropic, openai
--model NAME          Override model name (e.g., opus, sonnet, gpt-4o)
--max-steps N         Max actions before stopping (default: 15)
--app NAME            App name for component memory (default: desktop)

What It Does Internally

The agent runs an autonomous loop — you don't need to manage any of this:

  1. Observe — screenshot + UI detection + component matching
  2. Verify — check if the previous action succeeded
  3. Plan — decide the next action (click, type, scroll, etc.)
  4. Execute — perform the action
  5. Repeat — until task is done or max steps reached

The agent learns UI components on first encounter and reuses them in future sessions.

Trustgrade A

  • passBody integrity

    Whether the stored document is plausibly the kind of file the artifact declares, rather than something fetched by mistake.

  • passType matchnot applicable to this artifact type

    Whether the artifact is really the kind of thing its metadata claims it is.

  • passFreshness

    How long since the source repository was last pushed to.

  • passPrompt injection

    Scans the artifact's own text for instructions aimed at your agent rather than at you.

  • passLicense

    Whether the source repository declares an SPDX license permissive enough to redistribute.

How the grade is calculated

Each check contributes 0 points when it passes, 1 when it warns, and 2 when it fails. The total maps to a letter:

  • Aevery check passed
  • Bone warning
  • Ctwo warnings
  • Dprompt injection or body integrity failed, or three warnings
  • Fone of those failed, and something else is wrong

These are automated hygiene checks, not a security audit, and not a dependency or vulnerability scan. A grade of A means nothing was flagged — not that the artifact is safe.

Versions

  • git-45ba3f0a3b4b2026-07-31