LLM Wiki — Second Brain for Claude Code + Obsidian
SkillDocs & knowledgeThis skill lets your AI build and maintain a personal knowledge wiki in Obsidian from articles, papers, and notes you feed it. Instead of one-off summaries, your AI keeps a growing knowledge base up to date, so everything you learn stays organized and connected in one place over time.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then ask your AI to start or update your Obsidian wiki — phrases like "second brain" or "personal knowledge wiki" work. Feed it an article, paper, or note to begin building pages.
Then ask your AI: use the LLM Wiki — Second Brain for Claude Code + Obsidian skill
What your AI can do with it
- Turn articles, papers, and notes into wiki pages in Obsidian
- Update entity and concept pages as you add new sources
- Link related topics with cross-references between pages
- Keep an up-to-date synthesis of what you have learned
- Grow your knowledge base incrementally, a few sources at a time
What this skill tells your AI
The instructions your AI receives, as published by ar9av/obsidian-wiki in .skills/llm-wiki/SKILL.md and read by ahel’s review.
Inspired by Andrej Karpathy's LLM Wiki pattern (gist). This skill turns Claude Code (or any agent CLI) into a disciplined wiki maintainer that incrementally builds and maintains a persistent, interlinked Obsidian vault as you feed it sources. The knowledge compounds — cross-references, contradictions, and synthesis are already there when you query.
Core principle
Most LLM+docs workflows are RAG: retrieve fragments at query time, synthesize from scratch, forget. The wiki is compounding: sources are read once, integrated into a persistent markdown knowledge base, and kept current. You curate and ask; the LLM reads, files, cross-references, and maintains.
Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase.
When to use
- Personal: track goals, health, psychology, journaling, self-improvement
- Research: deep dives over weeks on a topic — papers, articles, reports, evolving thesis
- Book companion: file chapters as you read; build a fan-wiki-style companion for characters, themes, plot threads
- Business/team: internal wiki fed by Slack, meeting notes, calls — LLM does maintenance nobody else wants to do
- Competitive analysis, due diligence, trip planning, course notes, hobby deep-dives
Do NOT use when: you need one-shot Q&A over a fixed document (use RAG), you don't plan to add sources over time, or you don't want Obsidian in the loop.
Architecture (three layers)
vault/
├── raw/ # Layer 1 — IMMUTABLE source of truth
│ ├── <source files> # Articles, papers, PDFs, images, data
│ └── assets/ # Downloaded images from clipped articles
├── wiki/ # Layer 2 — LLM-owned knowledge base
│ ├── index.md # Content catalog (LLM updates every ingest)
│ ├── log.md # Append-only timeline (## [YYYY-MM-DD] <op> | <title>)
│ ├── entities/ # Person/Org/Place pages
│ ├── concepts/ # Ideas, theories, frameworks
│ ├── sources/ # One summary page per ingested source
│ ├── comparisons/ # Cross-source analysis pages
│ └── synthesis/ # High-level syntheses, theses, overviews
├── CLAUDE.md # Schema + conventions (Claude Code)
└── AGENTS.md # Same content, for Codex/Cursor/Antigravity
- Layer 1 (raw/) — you own. LLM only reads; never writes.
- Layer 2 (wiki/) — LLM owns. It creates, updates, and cross-references pages. You read it.
- Layer 3 (CLAUDE.md / AGENTS.md) — the schema. Conventions, workflows, frontmatter rules. Co-evolved by you and the LLM.
Three core operations
- Ingest — LLM reads a source, discusses takeaways with you, writes a source summary, updates 10-15 relevant pages, updates index, appends to log. See
references/ingest-workflow.md. - Query — LLM reads
index.mdfirst, drills into relevant pages, synthesizes with citations. Good answers get filed back into the wiki so explorations compound. Seereferences/query-workflow.md. - Lint — Health check: contradictions, stale claims, orphan pages, missing cross-refs, concepts mentioned but lacking their own page, data gaps to fill with web search. See
references/lint-workflow.md.
Quick start
# 1. Initialize a vault (in Obsidian's vault directory)
python scripts/init_vault.py --path ~/vaults/research --topic "LLM interpretability"
# 2. Drop a source into raw/, then ingest
/wiki-ingest ~/vaults/research/raw/anthropic-monosemanticity.pdf
# 3. Ask questions (answers can be re-filed into the wiki)
/wiki-query "how does monosemanticity compare to mechanistic interpretability?"
# 4. Periodic health check
/wiki-lint
# 5. See the timeline
/wiki-log --last 10
Slash commands (this plugin ships)
| Command | Purpose |
|---|---|
/wiki-init | Bootstrap a fresh vault with schema files + starter structure |
/wiki-ingest <path> | Read a source, discuss, update wiki, log it |
/wiki-query <question> | Search wiki, synthesize answer, offer to file back |
/wiki-lint | Run health check — contradictions, orphans, stale claims, gaps |
/wiki-log | Show recent log entries (uses unix tools on log.md) |
Sub-agents (this plugin ships)
| Agent | When dispatched |
|---|---|
wiki-ingestor | Delegated ingest flow — reads source, proposes updates, applies after your approval |
wiki-linter | Runs the health-check workflow independently, reports findings |
wiki-librarian | Answers queries using index-first search, synthesizes with citations |
Python tools (scripts/)
All tools are standard library only (no pip installs). Run with python scripts/<tool>.py --help.
| Script | Purpose |
|---|---|
init_vault.py | Create folder structure + seed CLAUDE.md, AGENTS.md, index.md, log.md |
ingest_source.py | Helper: extract text/frontmatter from a source file, ready for LLM review |
update_index.py | Regenerate index.md from wiki page frontmatter (category, date, source count) |
append_log.py | Append a standardized log entry ## [YYYY-MM-DD] <op> | <title> |
wiki_search.py | BM25 search over wiki pages (standalone fallback when index.md isn't enough) |
lint_wiki.py | Find orphans (no inbound links), stale pages, missing cross-refs, broken links |
graph_analyzer.py | Compute link graph stats — hubs, orphans, clusters, disconnected components |
export_marp.py | Render a wiki page (or subtree) to a Marp slide deck |
Cross-tool compatibility
The vault's schema lives in CLAUDE.md (Claude Code) or AGENTS.md (Codex/Cursor/Antigravity/OpenCode). The same content works in both. This plugin ships both templates. For per-tool setup instructions see references/cross-tool-setup.md.
CLAUDE.md → Claude Code
AGENTS.md → Codex CLI, Cursor, Antigravity, OpenCode, Gemini CLI
.cursorrules → legacy Cursor (pre-AGENTS.md)
The scripts are pure Python stdlib → run identically everywhere. Only the loader file changes per tool.
Obsidian setup (recommended)
- Obsidian Web Clipper — browser extension; converts web articles to markdown and drops them in
raw/ - Download images locally — Settings → Files and links → Attachment folder path =
raw/assets/. Settings → Hotkeys → bind "Download attachments for current file" toCtrl+Shift+D - Graph view — see hubs/orphans; essential for spotting structural problems
- Marp plugin — Markdown-based slide decks directly from wiki pages
- Dataview plugin — dynamic tables/lists over page frontmatter (tags, dates, source counts)
- Git — the vault is a plain markdown repo; version it
Full setup walkthrough: references/obsidian-setup.md
Why this works (vs plain RAG)
| Plain RAG | LLM Wiki |
|---|---|
| Rediscover knowledge each query | Knowledge accumulates |
| Cross-references re-computed every time | Cross-references pre-written and maintained |
| Contradictions surface only if you ask | Contradictions flagged during ingest |
| Exploration disappears into chat history | Good answers re-filed as new pages |
| Scales by embeddings infrastructure | Scales by markdown + index.md + optional local search |
At ~100 sources / hundreds of pages, index.md + filesystem search is enough. Past that, layer in a local search tool like qmd or use scripts/wiki_search.py.
Related skills (chains via context: fork)
This skill is marked context: fork so other skills can chain into it:
para-memory-files— PARA-method memory; complementary as long-term personal memory that feeds sources into the wikiobsidian-vault(mattpocock) — lightweight Obsidian note helper; this skill is the maintained-wiki layer on toprag-design— when wiki outgrows ~500 pages, use rag-design to bolt on a retrieval layermcp-design— expose the wiki as an MCP toolagent-communication— for multi-agent wiki maintenance (ingestor + linter + librarian)
Reference docs
references/wiki-schema.md— full vault layout, page frontmatter, naming conventionsreferences/page-formats.md— entity, concept, source, comparison, synthesis templatesreferences/ingest-workflow.md— the detailed ingest flow the wiki-ingestor agent followsreferences/query-workflow.md— query patterns, citation format, re-filing answersreferences/lint-workflow.md— health-check heuristicsreferences/obsidian-setup.md— Obsidian plugins, hotkeys, vault configreferences/cross-tool-setup.md— per-tool setup (Codex, Cursor, Antigravity, etc.)references/memex-principles.md— Bush's Memex, why the LLM changes the maintenance math
Templates (assets/)
CLAUDE.md.template,AGENTS.md.template,.cursorrules.template— schema loaders per toolindex.md.template,log.md.template— starter index and logpage-templates/— entity, concept, source-summary, comparison, synthesisexample-vault/— small worked example you can study or copy
Iron rule
The LLM never edits files in raw/. Ever. Sources are immutable. All LLM writes go to wiki/. If you need to correct a source, do it in raw/ yourself — then re-ingest.
Signals
- GitHub stars
- 3k
- Forks
- 338
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
llm-wiki- Source
- github.com/ar9av/obsidian-wiki