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 duriantaco · MCP Server · ★ 828
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is skylos safe to install? View the security audit →
Skylos Open-source, local-first checks for dead code, security issues, secrets, quality regressions, and AI-code mistakes before merge. Website | Docs | Repo Map | Quick Start | GitHub Action | VS Code Extension | Real-World Results | Benchmarks | Roadmap | Contributing English [Tra
| Stars | 828 |
| Forks | 55 |
| Language | Python |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 69.8011597928298/100 |
| Open Issues | 7 |
| Last Updated | 2026-09-21 |
| Created | 2025-04-28 |
| Platforms | mcp, python |
| Est. Tokens | ~4056k |
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skylos is Open-source Python, TypeScript, and Go SAST with dead code detection. Finds secrets, exploitable flows, and AI regressions. VS Code extension, GitHub Action, and MCP server for AI agents.. It is categorized as a MCP Server with 828 GitHub stars.
skylos is primarily written in Python. It covers topics such as ai-agents, ai-code-review, ai-generated-code.
You can find installation instructions and usage details in the skylos GitHub repository at github.com/duriantaco/skylos. The project has 828 stars and 55 forks, indicating an active community.
skylos is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to skylos on Agent Skills Hub include roam-code, Gito, vibe-coding-prompt-template. 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: