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 Prat011 · Claude Skill · ★ 1.7k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-llm-skills safe to install? View the security audit →
Awesome LLM Skills A curated list of awesome LLM Skills, resources, and tools for customizing AI workflows on tools like Claude Code, Codex, Gemini CLI, Qwen Code, OpenCode etc. Contents What Are LLM Skills? Quick Start Skills Platforms Claude Code (Anthropic) Claude Desktop (Anthropic) Codex CLI (OpenAI) Gemini CLI (Google) OpenCode (Open-source CLI) Qwen Code (Alibaba) Contributing Quick Contribution Steps Resources [Official Documentation](#official-d
| Stars | 1,749 |
| Forks | 303 |
| Language | Python |
| Category | Claude Skill |
| Quality Score | 64.5470398237202/100 |
| Open Issues | 163 |
| Last Updated | 2026-07-14 |
| Created | 2025-10-22 |
| Platforms | claude-code, cli, codex, gemini, python |
| Est. Tokens | ~19k |
These tools work well together with awesome-llm-skills for enhanced workflows:
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This repository contains documentation created to better understand the open project
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awesome-llm-skills is A curated list of awesome LLM and AI Agent Skills, resources and tools for customising AI Agent workflows - that works with Claude Code, Codex, Gemini CLI and your custom AI Agents. It is categorized as a Claude Skill with 1.7k GitHub stars.
awesome-llm-skills is primarily written in Python. It covers topics such as anthropic, awesome-list, awesome-lists.
You can find installation instructions and usage details in the awesome-llm-skills GitHub repository at github.com/Prat011/awesome-llm-skills. The project has 1.7k stars and 303 forks, indicating an active community.
The top alternatives to awesome-llm-skills on Agent Skills Hub include golutra, octopus, agentsys. 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: