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 AlphaGBM · Agent Tool · ★ 5.6k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is skills safe to install? View the security audit →
AlphaGBM Skills See what options are pricing in — with real data, not guesswork. 29 AI skills for options & research intelligence · Built on real market data · Trusted by 10,000+ traders Website · Documentation · Quick Start · Contributing 30-Second Demo Then ask your AI: "Analyze AAPL options using AlphaGBM" — works instantly with built-in data, no API key needed. What is AlphaGBM? AlphaGBM is a real-data options & research intelligence layer for traders and AI agents. Every number comes from real market data -- IV, Greeks, VRP, skew, flow, plus a tracked research workspace -- not LLM hallucination. These 26 skills bring AlphaGBM's capabilities into your AI workflow: Claude Code, Cursor, Windsurf, or any agent that supports skills. Why AlphaGBM?
| Stars | 5,582 |
| Forks | 337 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 71.9790736141111/100 |
| Last Updated | 2026-10-06 |
| Created | 2026-04-06 |
| Platforms | claude-code, python |
| Est. Tokens | ~16k |
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skills is Bring realtime market data and research workflows into Claude Code, Cursor & beyond — 29 open-source Skills for stocks, options and commodities.. It is categorized as a Agent Tool with 5.6k GitHub stars.
skills is primarily written in Python.
You can find installation instructions and usage details in the skills GitHub repository at github.com/AlphaGBM/skills. The project has 5.6k stars and 337 forks, indicating an active community.
skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to skills on Agent Skills Hub include open-saas, notebooklm-skill, agent-skills. 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: