openclaw

A computer-control agent that understands screenshots and interacts with graphical applications. It can carry out actions such as clicking buttons, filling forms, and navigating desktop software.

In plain words
What is it for?
Use it to automate desktop applications, complete multi-step workflows across apps, test graphical interfaces, and perform GUI tasks.
Why use it?
It helps with tasks that require a visual interface when no suitable command-line method is available.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/simular-ai/agent-s/openclaw
Any agent
npx skills add simular-ai/Agent-S --skill openclaw
Clone the repo
git clone --depth 1 https://github.com/simular-ai/Agent-S

Made for: Claude Code, Codex.

Per session 0 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 760 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00000 $0.00760
Opus 5 $0.00000 $0.00380
Sonnet 5 $0.00000 $0.00152
Haiku 4.5 $0.00000 $0.00076

Measured 2d ago against content hash e42a63f84807, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

openclaw 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (agent_s_wrapper.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

integrations/openclaw/SKILL.md · 103 lines

How it starts

The opening of the file, as written. The whole thing — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agent-S - Autonomous GUI Agent

Agent-S is a powerful autonomous agent that can control your computer's graphical interface to complete complex tasks. It combines vision and action understanding to interact with any GUI element.

What It Does

Agent-S can:

  • Navigate and interact with desktop applications
  • Fill forms, click buttons, and manipulate GUI elements
  • Complete multi-step workflows across different applications
  • Take screenshots and understand visual interfaces
  • Execute complex GUI automation tasks autonomously

When to Use

Use Agent-S when you need to:

  • Automate GUI-based tasks that don't have CLI alternatives
  • Interact with desktop applications programmatically
  • Complete workflows that require visual understanding
  • Perform actions across multiple applications
  • Test GUI interfaces

How to Invoke

Call the Agent-S wrapper via bash from the OpenClaw skills directory:

./agent_s_task "task description"

Or if installed in the default OpenClaw skills location:

~/.openclaw/workspace/skills/agent-s/agent_s_task "task description"

Note: Agent-S tasks can take 2-5 minutes to complete (up to 15 steps by default). The wrapper will wait for completion.

Parameters

  • task (required): Natural language description of the GUI task to complete
  • max_steps (optional): Maximum steps the agent can take (default: 15)
  • enable_reflection (optional): Enable self-reflection for better performance (default: true)

Examples

# Basic navigation
agent_s_task(task="Open Finder and create a new folder called 'Reports'")

# Form filling
agent_s_task(task="Open TextEdit, create a new document, and type 'Hello World'")

# Multi-step workflows
agent_s_task(task="Open Chrome, search for 'Python tutorials', and bookmark the first result")

# Application interaction
agent_s_task(task="Open System Preferences and check the current display resolution")

Technical Details

Agent-S uses:

  • Main Model: Claude Sonnet 4.5 for reasoning and planning
  • Grounding Model: UI-TARS-1.5-7B for visual grounding and coordinate extraction
  • Screen Resolution: Automatically scaled to 2400px max dimension
  • Platform Support: macOS, Linux, Windows

Read the full file on GitHub · 103 lines

Files

What ships with it

3 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.

Changes

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.

  1. 2d ago First seen · 103 lines · 0 tokens per session scan A e42a63f84807

Subscribe to this mod's changes

openclaw is a skill published in the GitHub repository simular-ai/Agent-S (12,211 stars, last pushed 1mo ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 760 tokens. 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.

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