No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by presidio-oss · Claude Skill · ★ 56
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is factif-ai safe to install? View the security audit →
Automate testing through AI-powered computer control. From manual steps to automated tests in minutes. 🚀 Factifai Factifai revolutionizes test automation by directly controlling your computer through AI. Using built-in vision capabilities of Claude, OpenAI, and Gemini along with computer use, it can navigate any application naturally - clicking, typing, and verifying results just like a human would. [!TIP] Looking for an agentic version? Check out https://factifai.io Table of Contents 🌟 Overview ✨ Key Features [📥 Getting Started](#-getting-s
| Stars | 56 |
| Forks | 27 |
| Language | TypeScript |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 64.7213440827773/100 |
| Open Issues | 4 |
| Last Updated | 2025-10-01 |
| Created | 2025-01-07 |
| Platforms | claude-code, cli, docker, gemini, node |
| Est. Tokens | ~7667k |
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factif-ai is AI-powered computer control for automated testing. Factifai uses vision models (Claude, GPT-4o, Gemini) to interact with applications naturally - clicking, typing, and verifying results just like a hu. It is categorized as a Claude Skill with 56 GitHub stars.
factif-ai is primarily written in TypeScript. It covers topics such as anthropic, automated-testing, automation.
You can find installation instructions and usage details in the factif-ai GitHub repository at github.com/presidio-oss/factif-ai. The project has 56 stars and 27 forks, indicating an active community.
factif-ai is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to factif-ai on Agent Skills Hub include lazy-bird, cai, os-ai-computer-use. 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.
Sources & who's responsible: