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 bh-rat · MCP Server · ★ 57
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is context-awesome safe to install? View the security audit →
context-awesome : awesome references for your agents A Model Context Protocol (MCP) server that provides access to all the curated awesome lists and their items. It can provide the best resources for your agent from sections of the 8500+ awesome lists on github and more then 1mn+ (growing) awesome row items. What are Awesome Lists? Awesome lists are community-curated collections of the best tools, libraries, and resources on any topic - from machine learning frameworks to design tools. By adding this MCP server, your AI agents get instant access to these high-quality, vetted resources instead of relying on random web searches. Perfect for : Knowledge worker agents to get the most relevant references for their work The source for the best learning resources Deep research can quickly gather a lot of high quality resources for any topic. Search agents https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11 Two Ways to Use Context Awesome | MCP Serv
| Stars | 57 |
| Forks | 10 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 73.2778351893218/100 |
| Last Updated | 2026-06-10 |
| Created | 2025-08-20 |
| Platforms | cli, mcp, node |
| Est. Tokens | ~22k |
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context-awesome is awesome-lists now available as CLI & MCP server for your agent.. It is categorized as a MCP Server with 57 GitHub stars.
context-awesome is primarily written in TypeScript. It covers topics such as agents, awesome, awesome-list.
You can find installation instructions and usage details in the context-awesome GitHub repository at github.com/bh-rat/context-awesome. The project has 57 stars and 10 forks, indicating an active community.
context-awesome is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to context-awesome on Agent Skills Hub include metorial-index, FirstData, Awesome-AI-For-Security. 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: