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 NanoNets · MCP Server · ★ 4.8k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is Graft safe to install? View the security audit →
Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase. <img src="https://img.shields.io/badge/License-MIT-20C997?style=for-the-badge"
| Stars | 4,831 |
| Forks | 423 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 65.0970472556691/100 |
| Open Issues | 76 |
| Last Updated | 2026-08-25 |
| Created | 2026-07-03 |
| Platforms | claude-code, cli, codex, gemini, mcp, node |
| Est. Tokens | ~23k |
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Graft is Turbocharge Claude Code, Cursor, Codex, Gemini & every coding agent: faster, cheaper, with contextual understanding specific to your codebase.. It is categorized as a MCP Server with 4.8k GitHub stars.
Graft is primarily written in TypeScript. It covers topics such as ai-agents, anthropic, claude-code.
You can find installation instructions and usage details in the Graft GitHub repository at github.com/NanoNets/Graft. The project has 4.8k stars and 423 forks, indicating an active community.
Graft is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to Graft on Agent Skills Hub include Unity-MCP, antigravity-workspace-template, ai-guide. 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: