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 JMcrafter26 · Agent Tool · ★ 64
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-tools safe to install? View the security audit →
Awesome AI Tools A curated list of awesome AI tools and repositories on GitHub Contents Awesome AI Tools Contents Large Language Models AI Agents \& Autonomous Systems AI Assistants \& Chatbots RAG \& Document AI AI Workflow Platforms Voice \& Speech Face \& Video Manipulation Video Generation Image Generation Image Enhancement Music \& Audio Code Assistants Computer Vision AI Infrastructure Learning Resources Observability \& Evaluation Specialized AI Tools Archived Projects Contributing Large Language Models Run and deploy large language models locally or in the cloud. ollama - Get up and running with Llama 3.3, Mistral, Gemma 2, and other large language models. DeepSeek-V3 - Ne
| Stars | 64 |
| Forks | 36 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 55.850357075609/100 |
| Open Issues | 5 |
| Last Updated | 2026-09-21 |
| Created | 2023-04-10 |
| Platforms | python |
| Est. Tokens | ~18k |
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awesome-ai-tools is A list of AWESOME AI tools on Github. It is categorized as a Agent Tool with 64 GitHub stars.
awesome-ai-tools is primarily written in Python. It covers topics such as ai, ai-tools, awesome.
You can find installation instructions and usage details in the awesome-ai-tools GitHub repository at github.com/JMcrafter26/awesome-ai-tools. The project has 64 stars and 36 forks, indicating an active community.
The top alternatives to awesome-ai-tools on Agent Skills Hub include toolsdk-mcp-registry, awesome-prompt-engineering, awesome-prompt-engineering. 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: