Ingest diverse sources into a layered Markdown wiki with cross-links and provenance markers.
npx skills add https://github.com/huidge/hermes-skills --skill llm-wiki-huidge
Please help me install this Agent Skill. Skill: llm-wiki Source: https://github.com/huidge/hermes-skills/tree/main/research/llm-wiki Command: npx skills add https://github.com/huidge/hermes-skills --skill llm-wiki-huidge
Karpathy's LLM Wiki addresses the challenge of creating and maintaining a persistent, interlinked Markdown knowledge base that can ingest diverse sources, compose cross-referenced insights, and stay current for research tasks.
Ingest your first source and initialize the wiki at the configured path.
High-intent search queries and answers about installing and using this skill.
To build a persistent markdown knowledge base, ingest diverse sources like articles, PDFs, and transcripts into a structured wiki. The system automatically organizes content into layered architectures with raw sources, wiki pages, and schema for reliable search.
Cross-linking in an interlinked markdown wiki connects entities, concepts, and queries through automated references. The system enforces cross-link integrity by applying frontmatter-based metadata and provenance markers, ensuring consistent navigation between related research notes.
Yes, you can ingest PDFs and transcripts into a structured research wiki. The system processes these diverse sources and automatically structures their content into a layered wiki architecture, separating raw source data from composed wiki pages and schema.
The best way to lint a markdown wiki for contradictions is to use automated auditing queries on the fixed wiki architecture. Linting checks frontmatter-based metadata and cross-references to detect consistency issues and surface high-quality insights.
Yes, this markdown wiki tool supports frontmatter-based metadata for search. It enforces a fixed wiki architecture using this metadata alongside provenance markers, ensuring reliable linting, consistent cross-references, and accurate query results across research notes.
A layered content architecture separates raw sources from wiki pages and schema to maintain provenance and data integrity. This structure ensures that ingested articles and transcripts remain auditable while supporting reliable cross-linking and linting.
SebasArenClassify raw materials and generate wiki pages from PDFs, notes, and URLs.
unclehowellCreate and maintain a persistent interlinked markdown knowledge base.
jwintzCreate and edit Obsidian Flavored Markdown notes with wikilinks, embeds, callouts, and YAML frontmatter.
poz110Analyze, code, and verify fixes for urgent software bugs.
