Obsidian + Claude Code Complete Guide

Huashu's April 2026 guide documents how to run a personal knowledge base where Claude Code acts as full-time librarian. The core thesis: store knowledge in a format AI can read (local Markdown), and let AI handle organizing, connecting, and maintaining it. You record and think; AI indexes and links.

The guide validates the llm-wiki-pattern with practical implementation details. Huashu manages 2,000+ files across 9 workspaces using a hierarchy of CLAUDE.md files — a root-level router dispatches tasks to subdirectory-specific rulesets. He wrote 55 custom Skills before discovering that Obsidian's native features (bidirectional links, graph view, fuzzy search) already solve problems he'd been reimplementing.

Convergent Evolution

Three billion-dollar projects independently chose the same memory format:

• Manus (acquired by Meta for $2B): stores agent memory in task_plan.md and notes.md
• OpenClaw (355k GitHub stars): MEMORY.md for knowledge, SOUL.md for personality
• Claude Code: CLAUDE.md for project context, memory/ directory for long-term storage

All landed on plain Markdown files. The reasoning: near-zero latency, $0 cost, any text editor works, native Git support, no vendor lock-in. For personal knowledge bases under 400K characters (~200K words), direct file reading beats vector databases on every dimension.

Three-Layer Architecture

The guide proposes a raw → wiki → output flow:

raw/ holds immutable source material — articles, meeting notes, reading annotations, chat screenshots. Append only, never modify. This is the source of truth.

wiki/ holds AI-written structured articles organized by type: concepts/, entities/, topics/. Each article follows a SCHEMA.md specification that defines naming conventions, frontmatter template, tag taxonomy, wikilink rules, and article structure. The AI creates and maintains these; humans can correct.

output/ holds query products — reports, analyses, comparisons generated from wiki knowledge. Good outputs feed back into wiki as new entries.

SCHEMA.md turns a general-purpose LLM into a consistent wiki curator. Without it, AI might use #ai-tools today and #AI-Tools tomorrow. With it, formatting stays predictable.

Six Design Principles

1. Markdown only — convert everything to .md before it enters the vault; keep originals outside
2. Consistent terminology — pick one form (RAG vs Retrieval-Augmented Generation) and stick to it; put the glossary in CLAUDE.md
3. Flat first — no more than 3 folder levels; use tags and links for categorization, folders for lifecycle (active/archive)
4. Every note needs a summary — one frontmatter sentence so AI can skip the full read when scanning
5. Five frontmatter fields — title, tags, created, type (fleeting/literature/permanent), summary
6. Separate human from AI output — different folders or filename prefixes; different trust levels, different modification rules

The type field borrows from Zettelkasten: fleeting notes (jotted thoughts), literature notes (reading summaries), permanent notes (refined opinions). AI can skip fleeting notes and prioritize permanent ones.

Workflows

Seven practical workflows, each requiring minimal human effort:

• Daily Notes + Weekly Review: write 3-5 lines daily, have AI generate weekly summaries
• Reading Notes: dump highlights into raw/, AI generates structured literature notes with cross-references to existing vault content
• Research Accumulation: save raw research, AI compiles into wiki articles, future research on the same topic starts with context
• Writing from Notes: describe topic in one sentence, AI searches vault and drafts from your existing material
• Project Management: one folder per project with index.md, completed projects move to archive/
• Auto-Organizing Old Notes: batch-process unsorted files, AI adds frontmatter and links
• Automatic Backlinks: AI scans daily notes, identifies entities, replaces plain text with [[wikilinks]], creates stub pages for missing entities

The last workflow — "Agentic Note-Taking" — is the most ambitious. Stefan Imhoff used to spend 10-15 minutes daily adding links by hand. With Claude Code, it takes seconds.

Tooling

obsidian-skills (by kepano, Obsidian's CEO) teaches Claude Code to handle Obsidian-specific syntax: [[wikilinks]], callout blocks, .canvas files, frontmatter YAML. Install with claude install kepano/obsidian-skills.

Claudian embeds Claude Code's chat interface directly in Obsidian's sidebar — no terminal switching.

Smart Connections builds a semantic index using AI embeddings. For vaults over 400K characters, this supplements direct reading.

MCP is unnecessary for most users. Claude Code already reads/writes local files directly — MCP adds a middle layer with no benefit unless you need Claude Desktop (not Claude Code) to access the vault, or you need to invoke Obsidian plugin features like Dataview queries.

Scale Thresholds

Claude's 200K-token context holds ~500K-800K Markdown characters. For most personal knowledge bases, that's enough to never need vector retrieval.

Relation to Existing Setup

This vault already implements Template C (Karpathy Wiki) from the guide:

• sources/ ≈ raw/
• wiki/ ≈ wiki/
• CLAUDE.md defines schema inline rather than in a separate SCHEMA.md

The guide suggests improvements worth considering:

• A glossary section in CLAUDE.md to enforce terminology
• The type: fleeting | literature | permanent distinction for wiki pages
• Auto-linking workflows for daily notes
• Separating AI output into its own directory or prefix

The core insight: "AI isn't your search engine — it's your knowledge compiler." RAG re-derives every answer from scratch. The wiki approach compiles raw material into persistent articles that accumulate and cross-reference over time.