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 awesomelistsio · Agent Tool · ★ 212
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-prompt-engineering safe to install? View the security audit →
Awesome Prompt Engineering A curated list of resources, tools, papers, and platforms for prompt engineering in large language models (LLMs) and generative AI. Prompt engineering is the craft of designing effective prompts to instruct and guide AI models to perform specific tasks accurately, creatively, and reliably. Contents Guides & Introductions Courses & Tutorials Prompt Libraries Prompt Engineering Tools Research & Papers Communities & Blogs [Applications & Use Cases](#appli
| Stars | 212 |
| Forks | 25 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 61.2987477385646/100 |
| Open Issues | 5 |
| Last Updated | 2026-03-12 |
| Created | 2025-06-28 |
| Platforms | python |
| Est. Tokens | ~1k |
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awesome-prompt-engineering is A curated list of resources, tools, papers, and platforms for prompt engineering in large language models (LLMs) and generative AI.. It is categorized as a Agent Tool with 212 GitHub stars.
awesome-prompt-engineering is primarily written in Python. It covers topics such as ai, awesome, awesome-list.
You can find installation instructions and usage details in the awesome-prompt-engineering GitHub repository at github.com/awesomelistsio/awesome-prompt-engineering. The project has 212 stars and 25 forks, indicating an active community.
The top alternatives to awesome-prompt-engineering on Agent Skills Hub include awesome-prompt-engineering, toolsdk-mcp-registry, 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: