llm-wiki

Ingest raw sources into interlinked markdown entity and concept pages.

Updated Aug 23, 2026
One-click install
npx skills add https://github.com/rtl-buddy/rtl-buddy-cdc --skill llm-wiki-rtl-buddy
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/rtl-buddy/rtl-buddy-cdc/tree/main/.claude/skills/llm-wiki
Command: npx skills add https://github.com/rtl-buddy/rtl-buddy-cdc --skill llm-wiki-rtl-buddy

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pyyaml, and includes scripts (resource) components.

What problem does it solve?

This Skill solves the problem of fragmented, ephemeral knowledge by providing a structured, agent-assisted system to build a compounding, persistent markdown-based knowledge base that evolves with your research.

Core Features & Use Cases

  • Persistent Knowledge Synthesis: Compiles information into interlinked markdown files, ensuring knowledge is retained and cross-referenced rather than rediscovered per query.
  • Automated Maintenance: Includes built-in linting to detect broken links, stale content, and schema violations, keeping the wiki healthy and consistent.
  • Use Case: Researchers can ingest diverse sources like papers, transcripts, and articles into a unified wiki, allowing the agent to maintain entity pages, concept definitions, and side-by-side comparisons automatically.

Quick Start

Use the llm-wiki skill to initialize a new knowledge base in the current directory and ingest the provided research article.

Frequently Asked Questions about llm-wiki

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a persistent markdown knowledge base from research sources?

To build a persistent markdown knowledge base, ingest raw sources like papers and articles so the system can synthesize them into structured, interlinked entity and concept pages within a local directory. This ensures long-term information retention and cross-referencing rather than rediscovering data per query.

What is the best way to maintain an interlinked wiki and prevent stale content?

The best way to maintain an interlinked wiki is using automated health-check linting to detect broken links, stale content, and schema violations. Log-based activity tracking further ensures the knowledge base remains healthy and consistent over time as new research is added.

Can I automatically generate entity and concept pages from raw research articles?

Yes, you can automatically generate entity and concept pages by ingesting raw research articles into the defined directory structure. The system compiles and synthesizes diverse source formats into interlinked markdown files, creating side-by-side comparisons and definitions automatically.

Do I need to follow a specific schema for markdown documentation and research synthesis?

Yes, you need consistent adherence to a defined schema for markdown documentation and research synthesis. This structured approach allows the system to maintain organized entity pages, track activity via logs, and run effective automated linting checks.

What are the limitations of using a local directory structure for a knowledge base?

Using a local directory structure for a knowledge base limits access to your local environment and requires manual synchronization for remote collaboration. You must also consistently adhere to the defined schema and run linting checks to prevent broken links and stale content.

Does llm-wiki require any special dependencies to set up a documentation wiki?

The llm-wiki skill requires the pyyaml dependency to set up a documentation wiki. It operates on local directory structures and uses built-in scripts to ingest raw sources, synthesize markdown files, and execute automated health-check linting.