Verified:GitHub

llm-wiki

A functional and flexible system for storing, retrieving, and managing all your digital information. Featuring customizable dashboards, robust privacy options, smart automation workflows and seamless integration with your favorite tools and devices. Available for Windows, macOS, Linux, and web/mobile platforms.

Updated May 5, 2026

Search Tags:#knowledge base #schema validation #RAG alternative #wikilinks #entity tracking #research ingestion #markdown wiki

Ready to run

npx skills add https://github.com/Z43L/zeus-agent --skill llm-wiki-z43l

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/Z43L/zeus-agent/tree/main/skills/research/llm-wiki
Command: npx skills add https://github.com/Z43L/zeus-agent --skill llm-wiki-z43l

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It eliminates the slow, repetitive work of turning scattered research into a persistent, cross-referenced knowledge base that improves over time instead of starting over on every question.

Core Features & Use Cases

Creates and maintains an interlinked markdown wiki for entities, concepts, comparisons, and saved query results.
Ingests new sources into raw layers and synthesizes durable wiki pages while keeping provenance and consistency.
Supports orientation, linting, and maintenance workflows (schema/index/log reading, orphan checks, broken wikilinks, frontmatter validation, and contradiction flags).

Quick Start

Use llm-wiki to initialize your knowledge base by asking the agent to create a wiki at your configured wiki path and start with a specific research domain.

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 scattered research sources?▼

To build a persistent markdown knowledge base, you can ingest new sources into raw layers and synthesize them into durable, interlinked wiki pages. This process preserves provenance and cross-references, allowing your research to compound across sessions instead of starting over.

What is the best way to maintain interlinked markdown wiki pages and prevent duplication?▼

Maintaining an interlinked markdown wiki involves performing orientation reads before ingesting or querying data to prevent duplication. You can also run linting workflows to check for orphan pages, broken wikilinks, frontmatter validation, and contradiction flags to ensure consistency.

Do I need a specific folder structure to use a markdown wiki for entity tracking?▼

Yes, entity tracking with a markdown wiki requires a specific folder structure containing SCHEMA.md, index.md, and log.md. The system uses a configured $WIKI_PATH, defaulting to ~/wiki, to store and organize your interlinked markdown files.

How does an interlinked markdown wiki compare to a standard RAG setup for research ingestion?▼

An interlinked markdown wiki serves as a RAG alternative by synthesizing durable pages with explicit wikilinks and schema validation rather than relying solely on vector retrieval. This approach preserves cross-references and provenance, compounding learning across sessions with built-in linting.

Can I check my markdown wiki directory for broken wikilinks and schema validation issues?▼

Yes, you can check a markdown wiki directory for broken wikilinks and schema validation issues by running linting and health-checking workflows. This maintenance process identifies orphan pages, validates frontmatter against SCHEMA.md, and flags contradictions to ensure knowledge base consistency.

Why does my markdown knowledge base lose context and cross-references across different sessions?▼

A markdown knowledge base loses context without persistent interlinked pages and orientation reads. By applying schema validation and maintaining an index with logs during research ingestion, the system preserves cross-references and compounds learning across multiple sessions.