by talentsache · Agent Tool · ★ 77
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AIPointer ⦿ The AI cursor companion. Hold a key, ask a question, get an answer about whatever your cursor is pointing at. macOS Apple Silicon · macOS Intel · Windows · Linux 🖱️ If AIPointer ⦿ is useful to you, a star 🌟 on GitHub helps the project stay alive. What it is AIPointer is an open-source desktop overlay. You hold a key (default: Right-Cmd on macOS, Right-Ctrl on Windows/Linux), a glassmorphism box pops up next to your cursor, you ask a question, and a vision-capable LLM answers about whatever's around the cursor. A screenshot of the cursor region, your prompt, and (optionally) your clipboard get sent to the provider you conf
| Stars | 77 |
| Forks | 2 |
| Language | TypeScript |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 61.3088991020193/100 |
| Last Updated | 2026-05-16 |
| Created | 2026-05-13 |
| Platforms | gemini, node |
| Est. Tokens | ~74k |
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aipointer is The AI cursor companion. Hold a key, ask a question, get an answer about whatever your cursor is pointing at. Vision LLM overlay for macOS, Windows, Linux. Multi-provider (OpenRouter, Anthropic, OpenA. It is categorized as a Agent Tool with 77 GitHub stars.
aipointer is primarily written in TypeScript. It covers topics such as ai, ai-assistant, ai-tools.
You can find installation instructions and usage details in the aipointer GitHub repository at github.com/talentsache/aipointer. The project has 77 stars and 2 forks, indicating an active community.
aipointer is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to aipointer on Agent Skills Hub include aipointer, palot, daymon. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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