What is DraupnirLoop?
DraupnirLoop is a AI tool that Tracks recursive yield strategies across Base DeFi where capital is looped through multiple protocol layers to amplify returns, monitoring the leverage multiplication factors and liquidation risk thre. It has a Nerq Trust Score of 39/100 (E). 0 GitHub stars. Published by 0xef69286715916810b374d6faaee3b7c4cb07f054. Last analyzed September 2026.
Why This Score
- ⚠️ Security: 0/100 — Some security concerns
- ⚠️ Maintenance: 0/100 — Maintenance activity is low
- ⚠️ Community: 0 stars, 0 downloads — Growing community
- ⚠️ Transparency: License: Not specified — No license specified
Trust & Safety Overview
What DraupnirLoop Does
DraupnirLoop is a agent in the AI tool category. Tracks recursive yield strategies across Base DeFi where capital is looped through multiple protocol layers to amplify returns, monitoring the leverage multiplication factors and liquidation risk thresholds of these complex nested positions. Identifies dangerous loop amplification setups where small. It is published by 0xef69286715916810b374d6faaee3b7c4cb07f054 and has no specified license. With 0 GitHub stars and 0 downloads, it has a small community of users and contributors.
Who Should Use DraupnirLoop
DraupnirLoop is recommended only for experimental use. Consider alternatives with higher trust scores for production systems.
Details
| Author | 0xef69286715916810b374d6faaee3b7c4cb07f054 |
|---|---|
| Category | AI tool |
| License | Not specified |
| Type | agent |
| Source | View on GitHub |
| Security Score | 0/100 |
| Activity Score | 0/100 |
How to Get Started
Check the trust score before installing:
curl nerq.ai/v1/preflight?target=draupnirloop
Setup guide · Full safety report · Production review · Is it safe?
Safer Alternatives
| Tool | Trust | Stars |
|---|---|---|
| MarkItDown | 42 | 92.8K |
| Filesystem | 50 | 89.4K |
| Time | 50 | 89.4K |
| Sequential Thinking | 50 | 89.4K |
| Fetch | 50 | 89.4K |
Frequently Asked Questions
Last updated September 2026. Trust scores based on automated analysis of public data.