agentmods.dev

desktop-agent-ops

A procedure for controlling native desktop applications and windows across operating systems when structured APIs, MCP servers, and native command tools cannot do the job. It uses accessibility information or screen reading to locate interface elements and verify actions.

In plain words
What is it for?
Use it to open and control desktop apps, enter text, click interface elements, and confirm that actions succeeded.
Why use it?
It provides a fallback for desktop tasks when no safer programmatic interface is available. Its checks help reduce mistakes caused by screen size, focus, or inaccurate click positions.

Skill for Claude CodeCodex

17 4mo ago A scan 38 tokens copy · 100% MIT
View the source file appergb/desktop-agent-ops
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/appergb/desktop-agent-ops/skill
Any agent
npx skills add appergb/desktop-agent-ops --skill skill
Clone the repo
git clone --depth 1 https://github.com/appergb/desktop-agent-ops

Made for: Claude Code, Codex.

Per session 38 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 5,098 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00038 $0.05098
Opus 5 $0.00019 $0.02549
Sonnet 5 $0.00008 $0.01020
Haiku 4.5 $0.00004 $0.00510

Measured 2d ago against content hash ff6cbabbccd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

desktop-agent-ops 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 37 executable files (scripts/accessibility_provider.py, scripts/ax_provider.py, scripts/cleanup_task.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.

Origin

This is a copy

100% identical to desktop-agent-ops — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

skill/SKILL.md · 426 lines

How it starts

The opening of the file, as written. The whole thing — 426 lines — stays where its author put it; the contents beside it link to each section on GitHub.

This is the detailed reference manual. For the quick operations guide, see desktop-agent-ops.md.

Desktop Agent Ops — Detailed Reference Manual

Table of Contents

Tool Priority

Use this skill ONLY as Priority 3 — after MCP servers/structured APIs (Priority 1) and native CLI/AppleScript (Priority 2). See desktop-agent-ops.md section 1 for the full decision framework.

Rule: Never use screen OCR to do what a structured API can do.

Auto-setup Gate

Run first_run_setup.py --check at session start. If not ready, run first_run_setup.py to auto-install all dependencies. Then set $PY. See desktop-agent-ops.md section 2 for details.

Core Execution Loop

FOCUS → LOCATE (accessibility/OCR) → BOUNDS-CHECK → MOVE → READBACK → EXECUTE → VERIFY

CRITICAL RULE: Click coordinates MUST come from accessibility or OCR output — NEVER from visual estimation of screenshots. Models frequently confuse left/right and misjudge pixel distances. Structured accessibility and OCR output return exact pixel coordinates.

CRITICAL RULE: Move → Readback → Click. Before every click, move the cursor first, read back mouse-position, verify the offset is ≤ 5px, then click. Never click without readback.

CRITICAL RULE: Re-locate before every click in multi-step tasks. Window positions, dialog states, and UI layouts change between steps. Never reuse coordinates from a previous step.

Read the full file on GitHub · 426 lines

Files

What ships with it

60 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 · 426 lines · 38 tokens per session scan A ff6cbabbccd4

Subscribe to this mod's changes

desktop-agent-ops is a skill published in the GitHub repository appergb/desktop-agent-ops (17 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 5,098 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to desktop-agent-ops, differing in 0 lines, and is treated as a copy.

Backend and APIsPlanning
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