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LLM Wiki Compiler A Claude Code and Codex-compatible plugin that compiles knowledge into a topic-based wiki — from scattered markdown files or entire codebases. Reduce context costs by 90% and give your agent a synthesized understanding of any project. Documentation What's New in v2.1 Codex-compatible plugin metadata — install from the same package root Skill-first Codex workflows — use natural prompts instead of Claude slash commands Shared session context helper — one wiki context renderer for Claude hooks and Codex guidance What's New in v2.0 Codebase mode — generate wikis from code repositories, not just markdown files Auto-detection — detects whether you're in a codebase or knowledge project Knowledge graph visualization — interactive canvas-based graph of your wiki Inspiration This plugin implements the LLM Knowledge Base pattern described by Andrej Karpathy: "Raw data from a given number of sources is collected, then compiled by an LLM into a .md wiki, then operated on by various CLIs by the LLM to do Q&A and to incrementally enhance the wiki, and all of it viewable in Obsidian. You rarely ever write or edit the wiki manually, it's the doma
| Stars | 323 |
| Forks | 35 |
| Language | HTML |
| Category | AI Tool |
| License | MIT |
| Quality Score | 67.4543130373343/100 |
| Open Issues | 1 |
| Last Updated | 2026-08-21 |
| Created | 2026-04-04 |
| Platforms | claude-code |
| Est. Tokens | ~24k |
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llm-wiki-compiler is Claude Code plugin that compiles markdown knowledge files into a topic-based wiki. Implements Karpathy's LLM Knowledge Base pattern.. It is categorized as a AI Tool with 323 GitHub stars.
llm-wiki-compiler is primarily written in HTML.
You can find installation instructions and usage details in the llm-wiki-compiler GitHub repository at github.com/ussumant/llm-wiki-compiler. The project has 323 stars and 35 forks, indicating an active community.
llm-wiki-compiler is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to llm-wiki-compiler on Agent Skills Hub include shaping-skills, pivotnacci, MedgeClaw. 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.
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