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 Pratiyush · MCP Server · ★ 362
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is llm-wiki safe to install? View the security audit →
llmwiki LLM-powered knowledge base from your Claude Code, Codex CLI, Cursor, Gemini CLI, and Obsidian sessions. Built on Andrej Karpathy's LLM Wiki pattern. 👉 Live demo: pratiyush.github.io/llm-wiki Rebuilt on every push from the synthetic sessions in . No personal data. Shows every feature of the real tool (activity heatmap, tool charts, token usage, model info cards, vs-comparisons, project topics) running against safe reference data.
| Stars | 362 |
| Forks | 53 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 62.6594464901934/100 |
| Open Issues | 17 |
| Last Updated | 2026-06-18 |
| Created | 2026-04-08 |
| Platforms | claude-code, cli, codex, gemini, mcp, python |
| Est. Tokens | ~22k |
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llm-wiki is LLM-powered knowledge base from your Claude Code, Codex CLI, Copilot, Cursor & Gemini sessions. Karpathy's LLM Wiki pattern — implemented and shipped.. It is categorized as a MCP Server with 362 GitHub stars.
llm-wiki is primarily written in Python. It covers topics such as ai, claude-code, cli.
You can find installation instructions and usage details in the llm-wiki GitHub repository at github.com/Pratiyush/llm-wiki. The project has 362 stars and 53 forks, indicating an active community.
llm-wiki is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-wiki on Agent Skills Hub include antigravity-workspace-template, jdocmunch-mcp, ai-devkit. 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: