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llm-wiki

Build persistent interlinked markdown knowledge bases with provenance tracking and wikilinks.

3|Updated Apr 21, 2026
Search Tags:#knowledge base#obsidian#provenance tracking#source ingestion#research notes#markdown wiki#rag alternative
One-click install
npx skills add https://github.com/DarkArty07/Aether-Agents --skill llm-wiki-darkarty07
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/DarkArty07/Aether-Agents/tree/main/home/skills/research/llm-wiki
Command: npx skills add https://github.com/DarkArty07/Aether-Agents --skill llm-wiki-darkarty07

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traditional RAG systems re-discover and re-process knowledge from scratch for every query, leading to redundant work, missed cross-references between related information, and inconsistent synthesized answers. This skill eliminates that overhead by letting you build a persistent, compounding interlinked markdown knowledge base that maintains cross-references, flags contradictions, and keeps all ingested knowledge up to date over time.

Core Features & Use Cases

  • Persistent Compounding Knowledge Base: Store all domain knowledge as interlinked markdown files that work out of the box with Obsidian, VS Code, and other editors, with no external database or specialized tooling required.
  • Automated Source Ingestion: Ingest web articles, PDFs, transcripts, and pasted text with automatic provenance tracking, drift detection for updated sources, and schema-constrained page creation to avoid duplicates and noise.
  • Built-in Health & Consistency Checks: Run lint audits to catch orphan pages, broken wikilinks, stale content, unresolved contradictions, and tag taxonomy violations to keep your wiki accurate and navigable.
  • Use Case: A researcher tracking AI/ML developments can use this skill to ingest new arxiv papers, automatically create and update entity pages for models and researchers, cross-reference related concepts, and get fast, cited answers to domain questions without re-reading every source from scratch.

Quick Start

Use the llm-wiki skill to initialize a new interlinked markdown knowledge base for your AI research domain, or ingest the latest arxiv paper you shared into your existing wiki.

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 without redundant RAG processing?▼

To build a persistent markdown knowledge base without redundant RAG processing, you can use automated source ingestion to create interlinked markdown files with automatic provenance tracking, eliminating the need to re-discover knowledge from scratch for every query.

Why does my RAG system miss cross-references and give inconsistent synthesized answers?▼

RAG systems miss cross-references and give inconsistent answers because they re-process knowledge from scratch per query. A compounding interlinked markdown wiki solves this by maintaining persistent cross-references and flagging contradictions across ingested sources.

Can I use Obsidian to navigate a markdown wiki generated from research notes?▼

Yes, you can use Obsidian to navigate a generated markdown wiki because the skill creates Obsidian-compatible wikilinks, allowing standard navigation of cross-referenced entities and concepts within your research notes without specialized tooling.

What is the best way to ingest web articles and PDFs into a personal knowledge management system?▼

The best way to ingest web articles and PDFs into a personal knowledge management system is using automated source ingestion with schema-constrained page creation, which automatically tracks provenance and detects drift for updated sources.

How do I track provenance and flag contradictions in an interlinked markdown wiki?▼

You track provenance and flag contradictions in an interlinked markdown wiki through built-in health and consistency checks, running lint audits to catch unresolved contradictions, broken wikilinks, and stale content during source ingestion.

Does this knowledge base approach require an external database to store domain intelligence?▼

No, this knowledge base approach does not require an external database to store domain intelligence because it uses persistent compounding markdown files that work out of the box with standard editors like VS Code and Obsidian.

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