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 hashgraph-online · MCP Server · ★ 438
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-ai-plugins safe to install? View the security audit →
Awesome Codex Plugins A curated list of awesome OpenAI Codex plugins, skills, and resources. OpenAI documents plugins and skills for Codex, packaging skills, MCP servers, and app integrations into shareable, installable bundles across the Codex app, CLI, and IDE extensions. Contents Start Here Official Plugins Community Plugins Plugin Development Guides & Articles Related Projects Start Here New plugin workflow:
| Stars | 438 |
| Forks | 498 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 67.8153674601444/100 |
| Open Issues | 71 |
| Last Updated | 2026-10-04 |
| Created | 2026-04-07 |
| Platforms | claude-code, codex, gemini, mcp, python |
| Est. Tokens | ~19k |
These tools work well together with awesome-ai-plugins for enhanced workflows:
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awesome-ai-plugins is A curated list of the best awesome AI plugins for AI assistants including Claude Code, OpenAI Codex / ChatGPT, Gemini, Antigravity, Pi / Oh My Pi, Grok, OpenCodek DSH, Kimi and More. https://hol.org/p. It is categorized as a MCP Server with 438 GitHub stars.
awesome-ai-plugins is primarily written in Python. It covers topics such as ai-assistant, ai-plugin, awesome.
You can find installation instructions and usage details in the awesome-ai-plugins GitHub repository at github.com/hashgraph-online/awesome-ai-plugins. The project has 438 stars and 498 forks, indicating an active community.
awesome-ai-plugins is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-ai-plugins on Agent Skills Hub include codexia, awesome-agent-skills, awesome-ai-agent-skills. 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: