chsh-sk-llm-wiki

Builds and maintains a persistent markdown knowledge base with interlinked wiki pages.

1|Updated Jan 30, 2026
One-click install
npx skills add https://github.com/chshzh/claude --skill chsh-sk-llm-wiki-chshzh
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: chsh-sk-llm-wiki
Source: https://github.com/chshzh/claude/tree/main/skills/chsh-sk-llm-wiki
Command: npx skills add https://github.com/chshzh/claude --skill chsh-sk-llm-wiki-chshzh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve? Traditional RAG rediscovers knowledge from scratch on every query, losing cross-references and synthesis work. This Skill builds a persistent Karpathy-style LLM Wiki — a directory of interlinked markdown files that compiles knowledge once and keeps it current, so contradictions are flagged and synthesis accumulates over time. ## Core Features & Use Cases - Source Ingestion: Capture URLs, PDFs, and pasted text into an immutable raw/ layer with sha256 drift detection, then synthesize cross-referenced entity and concept pages. - Domain Querying: Answer questions by reading the compiled wiki, citing pages, and filing valuable syntheses back into queries/ or comparisons/. - Wiki Linting: Audit for orphan pages, broken wikilinks, stale content, contradictions, tag taxonomy drift, and source integrity. - Use Case: A researcher tracking AI/ML papers ingests each new arxiv paper; the agent updates 5-15 interlinked pages, maintains the index and log, and answers future questions from the compiled knowledge instead of re-reading every source. ## Quick Start Ask the agent to initialize a new wiki for your research domain and ingest your first source URL into it.

Frequently Asked Questions about chsh-sk-llm-wiki

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I build a personal knowledge base with an AI agent?

Initialize a wiki directory with a SCHEMA.md defining your domain and tag taxonomy, then ingest sources one by one. The agent creates cross-referenced entity and concept pages, maintains an index and chronological log, and compounds knowledge across sessions.

How is an LLM wiki different from RAG?

RAG rediscovers knowledge from raw documents on every query, while an LLM wiki compiles knowledge once into interlinked markdown pages. Cross-references, contradiction flags, and synthesis persist between sessions instead of being recomputed.

Does the wiki work with Obsidian?

Yes, the wiki directory works 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 does the wiki detect when a 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, skips unchanged content, and flags drift when the source has silently changed.

What problems does wiki linting catch?

Linting 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, tag taxonomy violations, and source hash mismatches.

When should I not create a new wiki page?

Do not create pages for passing mentions or minor details. The schema's page thresholds require an entity or concept to appear in 2+ sources or be central to one source before warranting its own page.