skills.rest
swcstudiospace
swcstudiospaceOFFICIAL
Explore all skills by this author →
Website𝕏@govantarcticaGitHub

llm-wiki

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

Updated Aug 19, 2026
Search Tags:#markdown#research#knowledge base#obsidian#ingestion#wiki#rag-alternative
One-click install
npx skills add https://github.com/swcstudiospace/aimeecodes --skill llm-wiki-swcstudiospace
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-wiki
Source: https://github.com/swcstudiospace/aimeecodes/tree/main/.aimee/skills/llm-wiki
Command: npx skills add https://github.com/swcstudiospace/aimeecodes --skill llm-wiki-swcstudiospace

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-referenced, and kept current 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 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, 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 navigable. ## Quick Start Ask the agent to create a 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?▼

Set the WIKI_PATH environment variable or use the default ~/wiki directory, then ask the agent to initialize a wiki for your domain. The agent creates SCHEMA.md, index.md, and log.md, then ingests sources into raw/ while synthesizing cross-linked entity and concept pages.

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

RAG rediscovers knowledge from scratch per query, while the wiki compiles knowledge once into persistent markdown pages. Cross-references, contradiction flags, and synthesis persist across sessions instead of being recomputed each time.

Does the wiki work with Obsidian?▼

Yes, the wiki directory works as an Obsidian vault out of the box with wikilinks, graph view, and YAML frontmatter for Dataview. On headless servers, the obsidian-headless npm CLI syncs the vault via Obsidian Sync without a GUI.

How do I lint or audit a markdown wiki for broken links?▼

Ask the agent to lint the wiki. It scans for orphan pages with no inbound wikilinks, broken links to nonexistent pages, index completeness, frontmatter validation, stale content over 90 days old, contradictions, and source drift via sha256 comparison.

When should I not create a new wiki page?▼

Do not create pages for passing mentions or minor details outside the domain. 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.

Related Skills

Explore More Education & Research Skills→
SebasArenSebasAren
2

obsidian-wiki-ingest

Classify raw materials and generate wiki pages from PDFs, notes, and URLs.

Community
Advanced
siddsacharsiddsachar
1.4k

wiki_guide

Export memory graph data as Markdown with wiki-links and YAML frontmatter.

Community
Intermediate
ZurybrZurybr

obsidian-cli

Manage Obsidian vault notes, daily notes, tags, and sync via CLI.

Community
Intermediate
jhl-labsjhl-labs
50

RAG & Vector Search

Retrieve relevant documents and augment LLM prompts with vector search.

Official
Advanced