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 atlas-bear · Agent Tool · ★ 60
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is osint-ai-guide safe to install? View the security audit →
AI-Enhanced OSINT: A Practical Guide for Illicit Trade Intelligence Part of the Atlas Bear Labs intelligence methodology Intelligence Toolkit Integration: OSINT Tools - Maritime and supply chain intelligence gathering tools Recommended Reading - Essential reading on illicit trade and intelligence analysis Supply Chain Tools - Open source management solutions for emerging markets MARA Platform - Integrated maritime intelligence and incident monitoring A practical guide to navigating the integration of AI into OSINT tools and workflows. As artificial intelligence becomes increasingly embedded in intelligence gathering and analysis, this guide provides essential insights for effectively utilizing AI-enhanced capabilities while understanding their limitations and implications for traditional OSINT methodologies. []() []() []() Table of Contents BLUF [Executiv
| Stars | 60 |
| Forks | 8 |
| Category | Agent Tool |
| Quality Score | 51.3262104866945/100 |
| Last Updated | 2025-07-14 |
| Created | 2025-06-29 |
| Est. Tokens | ~24k |
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osint-ai-guide is Comprehensive guide to AI applications in OSINT workflows and intelligence analysis. It is categorized as a Agent Tool with 60 GitHub stars.
You can find installation instructions and usage details in the osint-ai-guide GitHub repository at github.com/atlas-bear/osint-ai-guide. The project has 60 stars and 8 forks, indicating an active community.
The top alternatives to osint-ai-guide on Agent Skills Hub include LocalLens, SkillX, relevanceai. 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.
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