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 TYH-labs · Claude Skill · ★ 270
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is unsloth-buddy safe to install? View the security audit →
unsloth-buddy /unsloth-buddy I have 500 customer support Q&As and want to fine-tune a summarization model. I only have a MacBook Air. <img src="https://img.shields.io/badge/Try%20It-1%20minute-black?style=for-the-badge" alt="Tr
| Stars | 270 |
| Forks | 13 |
| Language | Python |
| Category | Claude Skill |
| License | MIT |
| Quality Score | 70.2861770522735/100 |
| Open Issues | 1 |
| Last Updated | 2026-06-28 |
| Created | 2026-03-15 |
| Platforms | claude-code, cli, gemini, python |
| Est. Tokens | ~199k |
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unsloth-buddy is Zero-friction LLM fine-tuning skill for Claude Code, Gemini CLI & any ACP agent. Unsloth on NVIDIA · TRL+MPS/MLX on Apple Silicon. Automates env setup, LoRA training (SFT, DPO, GRPO, vision), post-hoc. It is categorized as a Claude Skill with 270 GitHub stars.
unsloth-buddy is primarily written in Python. It covers topics such as apple-silicon, claude-code, dpo.
You can find installation instructions and usage details in the unsloth-buddy GitHub repository at github.com/TYH-labs/unsloth-buddy. The project has 270 stars and 13 forks, indicating an active community.
unsloth-buddy is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to unsloth-buddy on Agent Skills Hub include hands-on-llm, ToolBrain, Travel-Agent-based-on-Qwen2-RLHF. 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: