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 frankchu91 · MCP Server · ★ 68
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mindbase safe to install? View the security audit →
MindBase — Karpathy's LLM Wiki, as a product An open-source implementation of Andrej Karpathy's LLM Wiki idea: an AI that builds and maintains a wiki from your sources. Not RAG-in-a-vector-DB. A real markdown wiki on your disk, that an LLM gardens for you between conversations. One command: starts the local server, opens the web app, and walks you through picking a free local model that fits your RAM. No API key, nothing leaves your machine. (Node 20+) MindBase implements Andrej Karpathy's [LLM-Wiki pattern](https://x.com/karpathy/s
| Stars | 68 |
| Forks | 11 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 51.4750320266457/100 |
| Last Updated | 2026-08-16 |
| Created | 2026-04-26 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~13k |
These tools work well together with mindbase for enhanced workflows:
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mindbase is Karpathy's LLM Wiki idea as a product — an AI that builds and maintains a markdown wiki from your notes and sources. MCP server + web UI, runs on free local models (Ollama), no API key needed. MIT.. It is categorized as a MCP Server with 68 GitHub stars.
mindbase is primarily written in TypeScript. It covers topics such as ai, karpathy, knowledge-base.
You can find installation instructions and usage details in the mindbase GitHub repository at github.com/frankchu91/mindbase. The project has 68 stars and 11 forks, indicating an active community.
mindbase is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mindbase on Agent Skills Hub include LLM-Wiki, matryca-plumber, Kompl. 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: