GitHub Copilot Wiki: An AI-Powered Second Brain Template

A forkable template for building LLM-maintained personal knowledge bases using GitHub Copilot.

Instead of starting from scratch on every query, Copilot incrementally builds and maintains a persistent, interlinked wiki of markdown files that compounds with every source ingested.

This project is an implementation of the LLM-Wiki concept popularized by Andrej Karpathy, designed to create a "Compounding Knowledge Pattern" through purely file-based local storage.

🏗️ Architecture: The Three Layers

The system is divided into three distinct layers of responsibility:

1. Raw Sources (raw/): Your collection of source documents. These are immutable—the AI reads them but never modifies them.
2. The Wiki (wiki/): Generating interlinked markdown files. The AI owns this layer—it writes it; you read it.
3. The Schema (.github/copilot-instructions.md): The "brain" or configuration layer. It tells the AI how to be a disciplined wiki maintainer rather than a generic chatbot.

🚀 10-Minute Setup Guide

1. Fork this repository: Click the "Fork" button to create your own copy.
2. Clone and Open in VS Code: Open the repo in an environment with GitHub Copilot installed.

💻 Terminal Quick Start (One-liners)

For rapid setup, you can skip the UI and use the terminal:

• Scaffold only (fastest): npx degit SriSatyaLokesh/copilot-llm-wiki#main my-wiki
• Fork and Clone (official): gh repo create my-wiki --template="SriSatyaLokesh/copilot-llm-wiki" --public --clone

⚙️ Configuration

1. Enable Prompt Files:
   • Open VS Code Settings (Ctrl+,).
   • Search for github.copilot.chat.promptFiles.
   • Set it to true.
2. Drop your first source: Put a markdown file or a URL reference in the raw/ directory.
3. Run Ingest:
   • In VS Code Chat: Type /ingest (or use the paperclip icon to attach .github/prompts/ingest.prompt.md) and provide- Batch Mode: Drop multiple markdown files in raw/ and run:

• Bash: ./.github/skills/wiki-ingest/scripts/intake.sh
• PowerShell: .\.github\skills\wiki-ingest\scripts\intake.ps1 ).

🧠 Core Workflows

📥 Ingest

Tell Copilot to "ingest ". It will:

• Fetch and save the source to raw/.
• Extract key facts.
• Create/update pages in wiki/entities/ and wiki/concepts/.
• Update the wiki/index.md and wiki/log.md.

🔍 Query

Ask a question about your knowledge domain. Copilot will:

• Read wiki/index.md to find relevant pages.
• Consult the interlinked wiki pages.
• Answer with citations.
• Offer to file the answer as a new QA page in wiki/qa/.

🧹 Lint

Run "lint the wiki" via lint.prompt.md or the librarian agent to check for orphans, broken links, or contradictions.

🛠️ Customization

1. Open .github/copilot-instructions.md.
2. Find the [YOUR DOMAIN] placeholders and replace them with your specific domain (e.g., "Medical Research", "Codebase Documentation", "Legal Case Files").
3. Customize the wiki/overview.md to reflect your project's goals.

🤖 Librarian Agent (CLI)

If you have the GitHub Copilot CLI installed, you can invoke the dedicated librarian agent:

copilot --agent librarian -p "ingest raw/new-data.md"

The librarian agent automatically decides between direct ingestion and using the intake scripts based on the complexity of your request.

📊 Visualization (Obsidian Recommended)

To get the most out of your LLM Wiki, we highly recommend using Obsidian to view your wiki/ directory.

• Knowledge Graph: Use Obsidian's "Graph View" to see how your entities and concepts are interconnected.
• Easy Navigation: Clickable links, back-links, and local previews make exploring your knowledge base feel like a "second brain."
• Markdown-Native: Since the wiki is 100% standard markdown, Obsidian handles it natively without any conversion needed.

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Inspired by Andrej Karpathy's llm-wiki. Built by the community.