llm-wiki

Compiles and maintains interlinked markdown knowledge bases from diverse sources with linting and logging.

1|Updated Jul 31, 2026
One-click install
npx skills add https://github.com/icyzh/hermes-web --skill llm-wiki-icyzh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/icyzh/hermes-web/tree/main/skills/research/llm-wiki
Command: npx skills add https://github.com/icyzh/hermes-web --skill llm-wiki-icyzh

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill solves the problem of fragmented knowledge by transforming scattered research, notes, and documents into a structured, compounding, and interlinked markdown knowledge base that evolves with your work.

Core Features & Use Cases

  • Compounding Knowledge: Automatically cross-references new information with existing pages to prevent duplication and ensure consistency.
  • Automated Maintenance: Includes built-in linting to identify broken links, orphan pages, and stale content, keeping your wiki healthy.
  • Use Case: Use this to maintain a research repository for AI/ML papers where the agent automatically links new paper summaries to existing concept pages and flags contradictory claims for your review.

Quick Start

Ask the agent to initialize a new wiki in your home directory and ingest the provided research article as the first source.

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 notes?▼

To build a persistent markdown knowledge base, compile and interlink diverse source materials using a defined schema. This automatically cross-references new information with existing pages to prevent duplication and ensure consistency.

How does automated cross-referencing work for an interlinked wiki?▼

Automated cross-referencing for an interlinked wiki works by compiling diverse source materials and automatically linking new entries to existing concept pages. It requires consistent adherence to a defined schema and log-based activity tracking to maintain structural coherence.

Does this knowledge base approach work for tracking entities across large collections of documents?▼

Tracking entities across large collections of documents is supported by compiling diverse source materials into a persistent markdown wiki. It facilitates research synthesis and entity tracking while using automated linting to ensure data integrity.

How do I identify broken links and orphan pages in a markdown wiki?▼

Identifying broken links and orphan pages in a markdown wiki is achieved through built-in automated linting. The maintenance feature flags broken links, orphan pages, and stale content to keep your research repository healthy and coherent.

What is the best way to maintain a research repository for AI and ML papers?▼

The best way to maintain a research repository for AI and ML papers is to use a persistent markdown knowledge base. It automatically links new paper summaries to existing concept pages and flags contradictory claims for your review.

Do I need a specific schema to maintain a markdown knowledge base?▼

A specific schema is required to maintain a markdown knowledge base effectively. Consistent adherence to this defined schema, alongside log-based activity tracking, ensures data integrity and structural coherence across all ingested research notes.