Borrowing it
Nothing to install: this file belongs to amichail-1/Orbination-AI-Desktop-Vision-Control. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/amichail-1/Orbination-AI-Desktop-Vision-Control/main/CLAUDE.mdgit clone --depth 1 https://github.com/amichail-1/Orbination-AI-Desktop-Vision-ControlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/amichail-1/orbination-ai-desktop-vision-control/claude-md)<a href="https://agentmods.dev/instructions/amichail-1/orbination-ai-desktop-vision-control/claude-md"><img src="https://agentmods.dev/badge/instructions/amichail-1/orbination-ai-desktop-vision-control/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/amichail-1/orbination-ai-desktop-vision-control/claude-md"><img src="https://agentmods.dev/badge/instructions/amichail-1/orbination-ai-desktop-vision-control/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00642 | $0.00642 |
| Opus 5 | $0.00321 | $0.00321 |
| Sonnet 5 | $0.00128 | $0.00128 |
| Haiku 4.5 | $0.00064 | $0.00064 |
Grade A, and why
Orbination-AI-Desktop-Vision-Control CLAUDE.md 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 10d ago.
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.
How it starts
The opening of the file, as written. The whole thing — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Desktop Control MCP - AI Usage Guide
Observation Priority (HOW TO SEE THE SCREEN)
Use text-based tools FIRST — they return exact text, coordinates, and element types. Screenshots are a LAST RESORT.
ocr_window— Read ALL visible text from a window with click coordinates. Use this FIRST to understand what's on screen. Returns exact text strings you can use in click_element, run_sequence, etc.get_window_details— Get UI elements (buttons, inputs, tabs) with types and coordinates. Use with kindFilter for specific element types.list_windows— See all open windows with visibility %. Use to find the right window title.scan_desktop— Full desktop overview: windows, elements, taskbar. Use at session start.screenshot_to_file— Visual screenshot. Use ONLY when text tools don't give enough context (e.g. visual layout, images, charts) or for final verification.
Action Priority (HOW TO INTERACT)
click_element/interact— Find by text, auto-detect type. Has UIAutomation + OCR fallback. Most reliable.click_menu_item— Navigate parent > child menus in one call.run_sequence— Batch multiple hotkeys, waits, focus changes in ONE call. For clicking buttons, preferclick_elementoverrun_sequence'socr_click.set_clipboard+keyboard_hotkey(ctrl+v) orpaste_text— For large text (XML, code, JSON).mouse_click x,y— Direct coordinate click. ONLY when text-based tools fail. Get coords fromocr_windoworget_window_detailsfirst.
Workflow Pattern
ocr_windoworget_window_details— understand what's on screen (text + coordinates)click_element/click_menu_item— click buttons and menus by textrun_sequence— batch keyboard actions (hotkey, wait, type, paste)ocr_window— verify result by reading text (NOT screenshot)screenshot_to_file— only for final visual verification if needed
Anti-Patterns (DO NOT)
- Do NOT use
screenshot_to_fileto understand UI — useocr_windoworget_window_detailsinstead - Do NOT screenshot after every action — use
ocr_windowto check state when needed - Do NOT guess button text — OCR the window first, then use exact text from OCR results
- Do NOT use
run_sequenceocr_clickfor buttons — useclick_elementwhich has better matching - Do NOT use
keyboard_typefor large text — usepaste_textorset_clipboard+ ctrl+v - Do NOT use
mouse_clickwith guessed coordinates — get coords fromocr_window/get_window_detailsfirst
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.
- 10d ago First seen · 35 lines · 642 tokens per session scan A c39f379bf773
Orbination-AI-Desktop-Vision-Control CLAUDE.md is an instructions file published in the GitHub repository amichail-1/Orbination-AI-Desktop-Vision-Control (8 stars, last pushed 6mo ago), licensed MIT. It adds 642 tokens to every session, about $0.0032 per session on Opus 5. 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 2026-08-31.
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