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 pplonski · Agent Tool · ★ 72
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is datasets-for-start safe to install? View the security audit →
📦 Datasets for Start A curated collection of simple, ready-to-use datasets for machine learning, data analysis, and tutorials. These datasets are designed to: ✅ be easy to load ✅ require minimal preprocessing ✅ work great for beginners and demos 🚀 Why this repo? This repository helps you: learn machine learning faster practice exploratory data analysis (EDA) build quick prototypes create tutorials and demos 👉 No heavy data cleaning — just start working with data. 🧠 Use with MLJAR Studio These datasets work perfectly with MLJAR Studio. MLJAR Studio is a desktop application designed for data science, combining AI and Python in one place. It lets users easily load data, build machine learning models, and generate reports without complex setup. It is especially beginner-friendly, helping users move from data to insights quickly while still giving advanced users full control. 👉 https://mljar.com/ 📊 Dataset Overview 🔵 Binary Classification tabular
| Stars | 72 |
| Forks | 80 |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 62.9133265975638/100 |
| Last Updated | 2026-08-20 |
| Created | 2017-03-30 |
| Est. Tokens | ~16k |
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datasets-for-start is A curated collection of simple, ready-to-use datasets for machine learning, data analysis, and tutorials.. It is categorized as a Agent Tool with 72 GitHub stars.
You can find installation instructions and usage details in the datasets-for-start GitHub repository at github.com/pplonski/datasets-for-start. The project has 72 stars and 80 forks, indicating an active community.
datasets-for-start is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to datasets-for-start on Agent Skills Hub include openwebui-extensions, osint-ai-guide, ClaudeR. 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: