llm-wiki

Compile and maintain a persistent, interlinked markdown knowledge base.

Updated May 4, 2026
One-click install
npx skills add https://github.com/InverterNetwork/hermes-agent --skill llm-wiki-inverternetwork
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/InverterNetwork/hermes-agent/tree/main/skills/research/llm-wiki
Command: npx skills add https://github.com/InverterNetwork/hermes-agent --skill llm-wiki-inverternetwork

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traditional RAG systems often rediscover information from scratch per query, leading to fragmented knowledge. This skill solves this by building a persistent, interlinked markdown knowledge base that compounds over time, ensuring consistency and deep synthesis.

Core Features & Use Cases

  • Persistent Knowledge Compounding: Maintains a structured wiki of entities, concepts, and comparisons that grows and improves with every interaction.
  • Automated Maintenance: Handles cross-referencing, schema enforcement, and health checks to prevent link rot and tag sprawl.
  • Use Case: Researchers or developers can use this to maintain a living, interlinked documentation vault for complex domains like AI architecture or personal project intelligence, where the agent acts as a curator that flags contradictions and synthesizes new sources.

Quick Start

Ask the agent to initialize a new wiki in your home directory to begin building your interlinked knowledge base.

Frequently Asked Questions about llm-wiki

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

FAQPage Schema
How do I maintain a persistent markdown knowledge base that compounds over time?▼

You maintain a persistent markdown knowledge base by compiling interlinked notes that synthesize information over time. This prevents fragmented recall by ensuring new interactions improve and expand a structured wiki of entities and concepts.

Why does my RAG system rediscover information from scratch and how does an interlinked wiki solve this?▼

Traditional RAG systems often rediscover information per query, leading to fragmented knowledge. An interlinked wiki solves this by maintaining a persistent knowledge base that compounds over time, ensuring consistency and deep synthesis instead of starting fresh.

How do I prevent link rot and tag sprawl in a markdown research vault?▼

You prevent link rot and tag sprawl in a markdown research vault through automated maintenance. The system handles cross-referencing, schema enforcement, and health checks to ensure data integrity and recall across your notes.

What is the best way to flag contradictions and synthesize new sources in domain-specific documentation?▼

The best way to flag contradictions and synthesize new sources is using an agent as a curator for your documentation vault. It monitors source drift, enforces schemas, and flags inconsistencies to maintain a living, interlinked knowledge base for complex domains.

Can I use this knowledge base approach for personal note-taking and research workflows?▼

Yes, you can use this knowledge base approach for personal note-taking and research workflows. It operates across research, personal documentation, and domain-specific synthesis to compile and maintain interlinked markdown notes for long-term recall.

Do I need a specific platform to initialize an interlinked knowledge base for my research notes?▼

No specific platform is needed to initialize an interlinked knowledge base for your research notes. You simply ask the agent to initialize a new wiki in your home directory to begin building your structured, interlinked markdown vault.