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 yzfly · Agent Tool · ★ 145
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-context-engineering safe to install? View the security audit →
Awesome Context Engineering A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs). Context engineering is the art and science of filling the context window with just the right information at each step of an agent's trajectory. 中文版本 | English 📚 Table of Contents What is Context Engineering? Featured Articles Research Papers Tools & Projects Expert Insights Model Context Protocol (MCP) Contributing Star History What is Context Engineering? Context Engineering is the systematic optimization of information payloads for Large Language Models (LLMs). It encompasses: Context Retrieval & Generation: Selecting and creating relevant information Context Processing: Organizing and structuring context for optimal consumption Context Management: H
| Stars | 145 |
| Forks | 53 |
| Category | Agent Tool |
| License | CC0-1.0 |
| Quality Score | 57.2639103099368/100 |
| Last Updated | 2026-09-20 |
| Created | 2025-07-28 |
| Platforms | claude-code |
| Est. Tokens | ~18k |
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awesome-context-engineering is A curated collection of resources, papers, tools, and best practices for Context Engineering in AI agents and Large Language Models (LLMs).. It is categorized as a Agent Tool with 145 GitHub stars.
You can find installation instructions and usage details in the awesome-context-engineering GitHub repository at github.com/yzfly/awesome-context-engineering. The project has 145 stars and 53 forks, indicating an active community.
awesome-context-engineering is released under the CC0-1.0 license, making it free to use and modify according to the license terms.
The top alternatives to awesome-context-engineering on Agent Skills Hub include awesome-agent-skills, claude-code-sub-agent-collective, ctxport. 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: