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 cortex-tms · Agent Tool · ★ 181
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is cortex-tms safe to install? View the security audit →
Cortex TMS Documentation Governance for AI Coding Agents ⭐ 166+ GitHub Stars | Open source, community-driven What is Cortex TMS? Cortex TMS scaffolds and validates governance documentation for AI coding agents. As AI models get more powerful and autonomous, they need clear, current governance docs to stay aligned with your project standards. The Challenge: Modern AI agents handle large context windows and can work autonomously—but without governance, they drift from your standards, overengineer solutions, and write inconsistent code. The Solution: Cortex TMS provides: 📋 Documentation scaffolding - Templates for PATTERNS.md, ARCHITECTU
| Stars | 181 |
| Forks | 7 |
| Language | MDX |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 69.7980044060804/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-09 |
| Created | 2026-01-11 |
| Platforms | claude-code, cli |
| Est. Tokens | ~618k |
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cortex-tms is The Universal AI-Optimized Project Boilerplate. A Tiered Memory System (TMS) designed to maximize AI agent performance. Includes an interactive CLI tool and a high-signal documentation standard.. It is categorized as a Agent Tool with 181 GitHub stars.
cortex-tms is primarily written in MDX. It covers topics such as ai, anthropic, architecture.
You can find installation instructions and usage details in the cortex-tms GitHub repository at github.com/cortex-tms/cortex-tms. The project has 181 stars and 7 forks, indicating an active community.
cortex-tms is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to cortex-tms on Agent Skills Hub include claudekit, agent-skills-cli, gptree. 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: