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 · ★ 105
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mindbase-llm-wiki 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 | 105 |
| Forks | 15 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.196553528671/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-04-26 |
| Platforms | browser, mcp, node |
| Est. Tokens | ~16k |
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mindbase-llm-wiki 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 105 GitHub stars.
mindbase-llm-wiki 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-llm-wiki GitHub repository at github.com/frankchu91/mindbase-llm-wiki. The project has 105 stars and 15 forks, indicating an active community.
mindbase-llm-wiki is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mindbase-llm-wiki on Agent Skills Hub include llm-wiki, LLM-Wiki, matryca-plumber. 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: