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llm-wiki

Build and maintain an interlinked markdown knowledge base from ingested sources.

Updated May 29, 2026
Search Tags:#note-taking#markdown#research#knowledge base#obsidian#wiki#rag-alternative
One-click install
npx skills add https://github.com/Reimonsk8/hermes-9router-model-balancing --skill llm-wiki-reimonsk8
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/Reimonsk8/hermes-9router-model-balancing/tree/main/scripts/skills/research/llm-wiki
Command: npx skills add https://github.com/Reimonsk8/hermes-9router-model-balancing --skill llm-wiki-reimonsk8

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Traditional RAG rediscovers knowledge from scratch on every query, losing cross-references and synthesis between sessions. This Skill maintains a persistent, compounding markdown wiki where sources are ingested once, cross-linked, and kept current, so accumulated knowledge is immediately queryable. ## Core Features & Use Cases - Source Ingestion: Capture URLs, PDFs, and pasted text into an immutable raw/ layer with sha256 drift detection, then synthesize entity, concept, and comparison pages with wikilinks and provenance markers. - Query & Synthesis: Answer domain questions by reading the index and relevant pages, citing wiki sources, and filing valuable answers back as query or comparison pages. - Wiki Linting: Audit for orphan pages, broken wikilinks, stale content, contradictions, tag taxonomy violations, and source drift, with severity-grouped reports. - Use Case: A researcher tracking AI/ML developments ingests arxiv papers and articles weekly; the agent updates entity pages for models and labs, flags contradictions between sources, and keeps the Obsidian-compatible vault synchronized across devices. ## Quick Start Ask the agent to create a new wiki about your chosen domain and ingest your first source URL into it.

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 markdown knowledge base with an AI agent?▼

Initialize a wiki directory with a SCHEMA.md defining conventions and tags, an index.md catalog, and a log.md action record. The agent then ingests sources into a raw/ folder and creates cross-linked entity and concept pages that compound over time.

How is an LLM wiki different from RAG?▼

RAG retrieves and re-derives knowledge from scratch per query, while the wiki compiles knowledge once into persistent markdown pages. Cross-references, contradiction flags, and synthesis already exist before you ask a question.

Does the wiki work with Obsidian?▼

Yes, the wiki directory functions as an Obsidian vault out of the box. Wikilinks render as clickable links, YAML frontmatter powers Dataview queries, and obsidian-headless enables sync on servers without a display.

How do I detect when a re-ingested source has changed?▼

Each raw file stores a sha256 hash of its body in frontmatter. On re-ingest of the same URL, the agent recomputes the hash and compares it, skipping unchanged content and flagging drift when the source has been modified.

What problems does a wiki lint check catch?▼

Lint finds orphan pages with no inbound links, broken wikilinks, pages missing from the index, invalid frontmatter, stale content over 90 days old, contradictions between pages, tags outside the taxonomy, and oversized pages needing splits.

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