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 simoncirstoiu · Agent Tool · ★ 382
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is alice safe to install? View the security audit →
ALICE Analyse · Learn · Ingest · Curate · Export All-in-one AI-powered image annotation, training, and dataset management toolkit. Works on both NVIDIA GPU and CPU. ALICE automatically detects your hardware — if no compatible GPU is found, it falls back to CPU mode. No manual configuration needed. Why? I needed a tool to train a YOLO model for my cameras, using my own images, with the specific angles and scenarios around my house. I couldn't find anything on the internet (or if it existed, I was probably too drunk to find it), so I built my own utility to fit my needs. If you find it useful — enjoy. If not, well... cry me a river! :) Quick Start The builder assembles from source modules, creates a with base dependencies, and patches the shebang so uses the venv automatical
| Stars | 382 |
| Forks | 39 |
| Language | JavaScript |
| Category | Agent Tool |
| Quality Score | 69.7333020399985/100 |
| Last Updated | 2026-04-26 |
| Created | 2026-04-19 |
| Platforms | node |
| Est. Tokens | ~600k |
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alice is Analyse · Learn · Ingest · Curate · Export — AI-powered YOLO dataset management toolkit. It is categorized as a Agent Tool with 382 GitHub stars.
alice is primarily written in JavaScript. It covers topics such as ai, ai-tools, annotation.
You can find installation instructions and usage details in the alice GitHub repository at github.com/simoncirstoiu/alice. The project has 382 stars and 39 forks, indicating an active community.
The top alternatives to alice on Agent Skills Hub include free-ai-resources-x, telekinesis-examples, UnrealClaude. 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: