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llm-wiki

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

2|Updated Oct 20, 2017
Search Tags:#note-taking#markdown#research#knowledge base#obsidian#wiki#rag-alternative
One-click install
npx skills add https://github.com/rbudiharso/dotfiles --skill llm-wiki-rbudiharso
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/rbudiharso/dotfiles/tree/main/hermes/.hermes/skills/research/llm-wiki
Command: npx skills add https://github.com/rbudiharso/dotfiles --skill llm-wiki-rbudiharso

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Traditional RAG rediscovers knowledge from scratch on every query, losing cross-references and synthesis. 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 pages, and filing valuable answers back into queries/ or comparisons/. - Wiki Linting: Audit for orphan pages, broken wikilinks, index gaps, stale content, contradictions, tag taxonomy violations, and source drift. - 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. ## Quick Start Ask the agent to create a new wiki for your research 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 SCHEMA.md, index.md, and log.md, then ingest sources one at a time. The agent creates entity and concept pages with wikilinks, updates the index, and logs every action for a compounding knowledge base.

What is the difference between an LLM wiki and RAG?▼

RAG retrieves and re-derives knowledge from raw documents on every query, while an LLM wiki compiles knowledge once into cross-referenced markdown pages. Contradictions, synthesis, and links persist between sessions instead of being rediscovered.

Does the llm-wiki skill work with Obsidian?▼

Yes, the wiki directory works as an Obsidian vault out of the box since it uses standard wikilinks and YAML frontmatter. It also supports obsidian-headless for syncing vaults on servers without a display.

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

Each raw source stores a sha256 hash of its body in frontmatter. On re-ingest of the same URL, the agent recomputes the hash and skips processing if identical or flags drift and updates if the content changed.

When should the agent not create a new wiki page?▼

Pages are only created when an entity or concept appears in two or more sources or is central to one source. Passing mentions and minor details are skipped to prevent page sprawl, per the thresholds defined in SCHEMA.md.

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