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 gameworkerkim · Agent Tool · ★ 338
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is vibe-investing safe to install? View the security audit →
vibe investing 인공지능을 도구로 활용한 바이브 투자(Vibe Investing) 큐레이션 · 전략 · 칼럼 · 논문 레포 AI를 엑셀과 같은 도구로 보고, 시장의 소음에서 신호를 가려내는 작업을 공개합니다. 인공지능, LLM은 만능이 아니며, 모델을 읽는 인간의 통찰력이 가장 중요하다고 믿습니다. 미국 나스닥·S&P500, 가상화폐, 명품 섹터, 크립토-주식 상관관계를 리서치하며 파과적 혁신 산업에서 알파 수익을 추구하고 있습니다. English README 이 레포가 향하는 곳 — 멀티-LLM 투자위원회 Vibe Investing의 다음 단계는 단일 LLM 의존에서 벗어나, 서로 다른 개성을 가진 여러 LLM을 하나의 퀀트 헤지펀드 투자위원회처럼 운용하고 교차검증하는 것입니다. ARDS — Adaptive Recession-Defensive Strategy 연구에서, 동일한 프롬프트와 동일한 시장 데이터를 Claude · Gemini · ChatGPT · DeepSeek 4개 모델에 입력했을 때 다음이 관찰되었습니다. 거시 진단은 수렴 — 4개 모델 모두 경기 침체 경고 구간으로 합의 (Composite 표준편
| Stars | 338 |
| Forks | 100 |
| Language | HTML |
| Category | Agent Tool |
| Quality Score | 56.1044338448007/100 |
| Last Updated | 2026-10-04 |
| Created | 2026-04-18 |
| Platforms | claude-code |
| Est. Tokens | ~18k |
These tools work well together with vibe-investing for enhanced workflows:
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vibe-investing is AI-powered Vibe Investing for NASDAQ, S&P500 & crypto: LLM quant trading tools, multi-agent backtesting, and data-driven market columns (mNAV arbitrage, BTC-Nasdaq coupling, Alpha Arena). 미국 주식·가상화폐 A. It is categorized as a Agent Tool with 338 GitHub stars.
vibe-investing is primarily written in HTML. It covers topics such as agentic-ai, ai-search, algorithmic-trading-dotnet.
You can find installation instructions and usage details in the vibe-investing GitHub repository at github.com/gameworkerkim/vibe-investing. The project has 338 stars and 100 forks, indicating an active community.
The top alternatives to vibe-investing on Agent Skills Hub include maverick-mcp, cryptocurrency.cv, free-crypto-news. 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: