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 Narwhal-Lab · AI Tool · ★ 311
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is MagicSkills safe to install? View the security audit →
🪄 MagicSkills: Build Skills Once, Reuse Them Across Every Agent Local-first skill infrastructure for multi-agent projects Turn scattered directories into a reusable · composable · syncable · callable shared capability library 🤖 Agent Apps 🧩 Agent Frameworks Claude Code · Cursor · Windsurf · Aider · CodexAny agent app that can read AutoGen · CrewAI · LangChain · LangGraph · HaystackSemantic Kernel · smolagents · LlamaIndexAny agent framework with tool / function integration support Initiated and maintained by Narwhal-Lab, Peking University <img src="https://img.shields.io/badge/Licens
| Stars | 311 |
| Forks | 23 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 64.0490405523813/100 |
| Open Issues | 13 |
| Last Updated | 2026-04-08 |
| Created | 2025-12-29 |
| Platforms | python |
| Est. Tokens | ~10049k |
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MagicSkills is MagicSkills：Stop copying skills between agents. MagicSkills turns scattered SKILL.md folders into reusable, composable, tool-ready capabilities.. It is categorized as a AI Tool with 311 GitHub stars.
MagicSkills is primarily written in Python. It covers topics such as agent, ai, function.
You can find installation instructions and usage details in the MagicSkills GitHub repository at github.com/Narwhal-Lab/MagicSkills. The project has 311 stars and 23 forks, indicating an active community.
MagicSkills is released under the MIT license, making it free to use and modify according to the license terms.
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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: