A template for building your own compounding, AI-maintained personal knowledge base — inspired by Andrej Karpathy's LLM Wiki pattern. Drop in raw notes, papers, or articles and let your AI assistant (Cursor, Claude Code, or VS Code/Copilot) transform them into a structured, interlinked wiki of plain Markdown files you can browse in any editor.
Most personal knowledge workflows stop at storage. This one goes further: the AI reads across your notes, finds connections, and builds a structured, cross-linked graph that grows with every new source you add.
No vector databases. No embeddings servers. No external API keys. Everything runs inside your IDE — Cursor, Claude Code, or GitHub Copilot — using your existing subscription. Three slash commands, plain Markdown files, nothing else to configure.
- Skill-driven and IDE-native —
/compile-wiki,/ask-wiki, and/lint-wikirun as slash commands directly inside Cursor, Claude Code, or VS Code/Copilot; no separate tools or services - No extra costs or API keys — uses your existing IDE plan; nothing to sign up for or pay for separately
- Automatic linking —
compile-wikiidentifies related concepts across all your notes and connects them with[[wiki-links]], so the knowledge graph emerges without manual curation - Graph-based retrieval —
ask-wikifollows wiki-links to traverse related pages; the LLM reads a compact index first, then drills into only the pages it needs — the full wiki never has to fit in context at once; works locally inside a single project or globally across all your projects, pointing at one central KB on your machine - Token-efficient by design — because only a relevant slice of the wiki is loaded per query, context stays lean even as the knowledge base grows; informal trials on a real personal KB showed roughly ~50% fewer tokens for small corpora and up to ~90% reduction for large ones compared to loading raw sources directly¹
- Private by default —
raw/andoutput/are gitignored; your notes stay on your machine - Plain Markdown, no tooling required — the
wiki/folder is just files; open it in any editor, IDE, or note-taking app; an optional Obsidian vault config is included for graph view and backlinks, but Obsidian is not required - One-command setup — symlink skills into your AI platform's directory with global (default) or local scope; works on macOS, Linux, and Windows
¹ These figures come from informal trials on my real knowledge base, not a formal RAG evaluation. Results will vary with corpus size, source density, and topic overlap. A proper evaluation (precision/recall, RAG benchmarks) could be added later — contributions welcome.
raw/ ← you drop source files here (papers, articles, notes, PDFs)
│
└─ /compile-wiki (AI skill)
│
▼
wiki/ ← AI builds and maintains this (plain Markdown)
│
├─ /ask-wiki → query your knowledge base, get reports saved to output/
└─ /lint-wiki → monthly health check: fix links, merge duplicates, suggest gaps
output/ ← generated briefings and reports (gitignored, stays local)
Each time you run /compile-wiki, the AI reads only the unprocessed files in raw/, extracts concepts, creates or updates topic pages in wiki/, links them with [[wiki-links]], and updates the index and log. Knowledge compounds with every pass.
macOS / Linux (also Git Bash or WSL on Windows):
git clone git@github.com:jlbgit/PersonalKnowledgeBaseCreator.git MyKnowledgeBase
cd MyKnowledgeBase
chmod +x setup.sh
./setup.sh cursor # Cursor: global scope (default)
./setup.sh cursor global # explicit global — same as above
./setup.sh claude global # Claude Code
./setup.sh copilot global # VS Code / GitHub Copilot
./setup.sh cursor claude global # multiple platforms at onceWindows (PowerShell):
git clone git@github.com:jlbgit/PersonalKnowledgeBaseCreator.git MyKnowledgeBase
cd MyKnowledgeBase
.\setup.ps1 cursor
.\setup.ps1 cursor global
.\setup.ps1 cursor, claude globalScopes: global (default) installs skills and writes wiki-config.md next to those skills (e.g. ~/.cursor/skills/wiki-config.md) so paths point at this clone — use /ask-wiki and /compile-wiki from any open project. local scaffolds a wiki in the current directory (see Project-local wikis below).
Windows note: Symlinks require Developer Mode (
Settings > System > For developers). The script falls back to copying files if unavailable — just re-run it aftergit pullto update.
Your data stays private. The setup script automatically disconnects your clone from the template repository so you can't accidentally push personal content back. Your
raw/files,output/reports, and any wiki pages you generate are all gitignored. To back up your knowledge base, add your own remote:git remote add origin <your-repo-url>.
Open AGENTS.md and replace the placeholder entries in the "My Interests / Focus Areas" section with your own topics. This tells the AI how to cluster and link concepts as it builds your wiki.
Drop any .md, .pdf, or .txt files into the raw/ folder.
Open the repo in your AI assistant and say:
"Compile the wiki" — or use the slash command
/compile-wiki
The wiki/ folder is plain Markdown — open it in any editor or IDE. If you use Obsidian, open it as a vault to get graph view, backlinks, and tag browsing out of the box.
After ./setup.sh cursor global (or claude / copilot), the skills read wiki-config.md next to the installed skills. It stores absolute paths to Wiki root, Wiki folder, Raw folder, and Output folder for this clone. Open any other repository in Cursor (or your assistant) and run /ask-wiki or /compile-wiki — the agent resolves your global wiki without that repo containing a wiki/ folder.
To keep a separate wiki inside another project (e.g. one codebase = one wiki), cd into that project and run the setup script from your clone of this template:
cd /path/to/YourOtherProject
/path/to/PersonalKnowledgeBaseCreator/setup.sh cursor localThis symlinks the skills (if needed) and creates in that directory: raw/, output/, wiki/ (with starter index.md, log.md, and .obsidian/), AGENTS.md, lint_graph.js (so /lint-wiki can run with cwd set to this folder), and a wiki-config.md in the project root.
Precedence: if the open workspace contains wiki-config.md at its root, the skills use that (local) and ignore the global file next to the skills. Remove or rename the local file to fall back to global.
| Skill | Trigger | What it does |
|---|---|---|
compile-wiki |
/compile-wiki |
Processes new raw/ files → creates/updates wiki pages → updates index and log |
ask-wiki |
/ask-wiki |
Answers questions using only your wiki → saves report to output/ → re-integrates insights |
lint-wiki |
/lint-wiki |
Health check: frontmatter validation, dangling links, orphan pages, duplicate topics, index sync, contradiction detection, new topic suggestions |
TL;DR: This pattern works best with ~100 raw sources producing ~hundreds of wiki pages — roughly 400,000 words of compiled knowledge. Modern LLMs on the Cursor plan handle this comfortably. Beyond ~300 sources the index starts getting heavy; at 500+ you should consider batching or adding a search layer.
These numbers come directly from Karpathy's original gist — "this works surprisingly well at moderate scale (~100 sources, ~hundreds of pages)" — and are validated by secondary analyses that estimate the resulting wiki at roughly 400,000 words (~500,000–550,000 tokens at ~1.3 tokens per word).
The skills use an index-first strategy: at query or lint time the LLM reads index.md (a compact catalog of all pages) and then drills into the specific pages it needs. The full wiki never has to fit in context at once — only the index plus a handful of pages do. This is why 400,000 words of accumulated knowledge is achievable even on models with 200K-token windows.
| Dimension | Sweet spot | Soft ceiling | What degrades |
|---|---|---|---|
Files per /compile-wiki run |
5–15 files | ~20–30 files | Response quality and accuracy drop as the agent juggles too many new sources at once |
Total sources in raw/ (accumulated over time) |
~100 | ~200–300 | index.md grows large and slow to scan |
| Single file size | < 30,000 words (~40K tokens) | < 50,000 words (~65K tokens) | Larger files may not leave enough context for the skill instructions + wiki index |
| Total wiki pages | ~100–200 pages | ~300–400 pages | index.md starts consuming significant context, leaving less room for reasoning |
Individual file sizes: Research papers (5,000–15,000 words) and web articles work perfectly. If you drop in a book, thesis, or large report, split it into chapters or sections first — anything over ~50,000 words in a single file risks crowding out the context window during compilation.
Batch your drops: Rather than adding 50 files at once, add 10–20 at a time and run
/compile-wikibetween batches. The skill is designed for incremental ingestion — it tracks what has already been processed inlog.md.
If your wiki grows past ~300 pages and /ask-wiki queries feel slow or imprecise, it may be time to add a lightweight search layer (e.g. qmd, which Karpathy recommends) rather than migrating to a full RAG pipeline. The underlying Markdown files stay the same — you're just adding a tool that lets the LLM pre-filter which pages to load.
PersonalKnowledgeBaseCreator/
├── AGENTS.md ← AI instruction file (edit your focus areas here)
├── README.md ← this file
├── LICENSE
├── .gitignore ← raw/, output/, and user wiki content stay local
├── setup.sh ← macOS/Linux installer (global | local)
├── setup.ps1 ← Windows installer (global | local)
├── lint_graph.js ← zero-dependency graph linter (Node.js)
├── raw/ ← drop source files here (gitignored, stays local)
├── output/ ← generated reports land here (gitignored, stays local)
├── skills/
│ ├── compile-wiki/SKILL.md
│ ├── ask-wiki/SKILL.md
│ └── lint-wiki/SKILL.md
└── wiki/ ← AI-maintained wiki (plain Markdown)
├── index.md ← master topic index
├── log.md ← processing audit trail
└── .obsidian/ ← optional Obsidian vault config (graph view, backlinks)
# After setup (not in this repo — paths depend on your machine):
~/.cursor/skills/ ← symlinks to skills/ + wiki-config.md (global install)
YourOtherProject/ ← optional: wiki-config.md at repo root (local install)
Once your wiki is populated, /ask-wiki lets you query across everything you've read. Here are some questions to try — swap in your own topics:
"Summarize the main approaches to building energy optimization I've collected. What gaps am I missing?"
"How do multi-agent LLM systems relate to the agentic AI patterns I've been reading about?"
"Which papers or sources connect Model Predictive Control to machine learning methods?"
"Give me a research briefing on token optimization techniques across all my notes."
"What do I know about confounding bias and causal inference? How does it connect to double machine learning?"
Each query saves a report to output/ and feeds new insights back into the wiki — so the knowledge base keeps compounding.
The graph below shows an example knowledge graph visualised in Obsidian. Node colors represent topic clusters; edges are [[wiki-links]] extracted by the AI.
./setup.sh --uninstall cursor
# or
.\setup.ps1 -Uninstall cursorThis removes the skill symlinks (or copies) and the global wiki-config.md next to them. Your wiki clone and any local wiki-config.md inside other projects are untouched — delete a local wiki-config.md yourself if you no longer want that project to override the global wiki.
This pattern is based on the LLM Wiki approach originally proposed by Andrej Karpathy. The core idea: use an LLM not as a search engine, but as a librarian that maintains a structured, growing knowledge graph from your raw inputs.
The implementation in this repository was also inspired by the work of Nick Spisak and the comprehensive LLM Wiki tutorial by Data Science Dojo.
MIT — see LICENSE.
Please review the DISCLAIMER.md file for important legal information regarding the use of this experimental software.
*tested on Cursor only as of April 2026