vault-compare

name	vault-compare
description	Compare the 4 knowledge management approaches and recommend the best fit for the user. Use when the user asks 'which approach should I use?', 'compare the approaches', or is unsure how to start.
allowed-tools	Read Glob Grep

Vault Compare

Help the user choose between the 4 Obsidian + AI knowledge management approaches.

Workflow

Step 1: Understand the user's situation

Ask about:

• Content type: Books? Articles? YouTube videos? Meeting notes? Code docs? Podcasts?
• Volume: How much content do they have? How fast does it grow?
• Primary use: Retrieval? Learning? Token reduction? Search?
• Technical comfort: CLI tools? Obsidian experience? Zettelkasten knowledge?
• Time budget: 5 minutes? A weekend? Ongoing commitment?
• Who consumes the output: Human browsing? AI agent? Both?

Step 2: Present the comparison

Use this scoreboard:

Dimension	Second Brain	Personal Search	AI Coding Vault	Karpathy LLM Wiki
Technical Complexity	6/10	4/10	6/10	2/10
ROI	7/10 setup, 5/10 long-term	8/10	7/10 (conditional)	9/10 setup, 7/10 long-term
Time to Value	Days	1 week	6-12 weeks	Minutes to hours
Primary Consumer	Human (thinking)	Human (retrieval)	AI agent (navigation)	Both human + AI
Maintenance	High (curation)	Low (~1hr/month)	Medium (session-end)	Low (periodic lint)
Failure Mode	Vault bloat + false confidence	Blind spots	Stale docs mislead agent	Index decay
Human Curation	AI extracts, human reviews	Human curates sources	Human defines structure	AI does everything
Infrastructure	CLI + Obsidian	Scripts + commands	SKILL.md + conventions	Just markdown

Step 3: Make a recommendation

Choose Karpathy's LLM Wiki if:
- You want to start in 5 minutes, not 5 days
- You have raw data piling up
- You want AI to handle all organization
- Your scale is personal (hundreds of pages)

Choose Personal Search Engine if:
- You already follow 5-15 trusted creators
- You want ROI this week
- You've tried Zettelkasten and abandoned it

Choose Second Brain Building if:
- You consume 2-4+ books/month
- You want AI that cites YOUR reading
- Your domain changes slowly

Choose AI Coding Vault if:
- You use Claude Code daily on a 3+ month project
- You've copy-pasted architecture explanations 5+ times
- Token costs are a real concern

Step 4: Present the layering strategy

If the user has multiple needs, recommend the layered approach:

Vault	Purpose	When to Start
Karpathy Wiki	Raw data → organized knowledge	First (lowest effort)
Mentor Vault (PSE)	Search trusted sources	When you have 5+ trusted sources
Project Vault (AI Coding)	Per-project agent memory	When a project hits 3+ months
Library Vault (Second Brain)	Book/course knowledge	When you have a book backlog

Start with one. Add others as needs grow.

Step 5: Karpathy LLM Wiki vs Semantic Search RAG

If the user asks about RAG, present this comparison:

Dimension	LLM Wiki	Semantic Search RAG
How it finds info	Reads indexes, follows links	Vector similarity search
Understanding	Deep relationships via links	"These chunks seem similar"
Infrastructure	Just markdown files	Embedding model + vector DB + chunking pipeline
Cost	Free (token costs only)	Ongoing compute + storage
Maintenance	Run a lint, add articles	Re-embed when things change
Sweet spot	Hundreds of pages	Millions of documents

Scale threshold: hundreds of pages with good indexes = wiki. Millions of documents = traditional RAG.