Distill knowledge into an Obsidian-based wiki using a three-layer architecture.
npx skills add https://github.com/zjykzk/blog --skill llm-wiki-zjykzk
Please help me install this Agent Skill. Skill: llm-wiki Source: https://github.com/zjykzk/blog/tree/main/.agents/skills/llm-wiki Command: npx skills add https://github.com/zjykzk/blog --skill llm-wiki-zjykzk
The LLM Wiki pattern provides a framework to build a persistent, compounding knowledge base that is distilled once and kept current, rather than re-derived on every query, using Obsidian as the workspace.
Set up your Obsidian vault, define the wiki schema, and begin distilling a small set of sources into the wiki.
High-intent search queries and answers about installing and using this skill.
Build a persistent Obsidian knowledge base by applying a three-layer architecture: distill raw sources into structured wiki pages, enforce a frontmatter-driven schema, and track provenance to keep knowledge current without re-deriving it on every query.
The three-layer architecture for knowledge distillation separates raw sources, the distilled wiki, and the governing schema. This structure enforces typed relationships and provenance tracking, ensuring compounding knowledge management rather than redundant data generation.
Set up an Obsidian wiki schema by defining a frontmatter-driven page structure that enforces typed relationships and provenance tracking. You then coordinate ingestion, querying, and linting as separate operations to maintain content governance.
Yes, the LLM wiki pattern provides explicit guidance for knowledge-management strategy, allowing you to structure both project-based and global content organization within your Obsidian vault to ensure clear provenance and cross-links.
Track provenance when distilling raw sources by applying a frontmatter-driven page schema and integrating references to canonical sources. This ensures all distilled knowledge within the three-layer architecture remains traceable and governed.
Avoid using a distilled wiki approach if your workflow requires re-deriving context dynamically on every query rather than maintaining a compounding knowledge base. This pattern relies on separating ingestion, querying, and linting into distinct operations.
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