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second-brain-mapping

Extract structured metadata from typed vault files without LLM runs.

31|20|Updated Apr 9, 2026
Search Tags:#insights#metadata#vault#knowledge-graph#wikilinks#cross-document#queryable
One-click install
npx skills add https://github.com/mycelium-hq/ai-brain-starter --skill second-brain-mapping
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: second-brain-mapping
Source: https://github.com/mycelium-hq/ai-brain-starter/tree/main/skills/second-brain-mapping
Command: npx skills add https://github.com/mycelium-hq/ai-brain-starter --skill second-brain-mapping

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

Unified vaults of typed notes often become a tangle of unstructured data. This skill turns that vault into a structured, queryable index and enables cross-type insights without requiring costly language-model runs for every extraction.

Core Features & Use Cases

  • Extracts and standardizes metadata from every typed file (books, meetings, people, articles, goals, etc.)
  • Optional knowledge-graph extraction and wikilinks to surface cross-document patterns
  • Cross-type insight engine to reveal relationships not visible in a single file
  • Zero LLM cost per run for metadata extraction and insights
  • Use to refresh your vault's queryable index or discover cross-doc patterns

Quick Start

Run /second-brain-mapping to generate metadata and cross-document insights from your vault.

Frequently Asked Questions about second-brain-mapping

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

FAQPage Schema
How do I extract metadata from multiple typed notes in my vault?▼

Cross-document insights are surfaced by an insight engine that analyzes standardized metadata and wikilinks across different document types to reveal relationships not visible within a single file.

Does metadata extraction from a vault require LLM costs per run?▼

Running the metadata extraction indexes your vault by executing a single non-LLM workflow command that extracts structured metadata and generates cross-type insights from your typed files.

Can I build a knowledge graph from wikilinks in my vault notes?▼

Vault mapping is best handled by deterministic extraction when you have large volumes of typed files like goals and articles, whereas LLM-based extraction suits unstructured notes but incurs higher costs.

What are the limitations of non-LLM metadata extraction for knowledge graphs?▼

Vault mapping is best handled by deterministic extraction when you have large volumes of typed files like goals and articles, whereas LLM-based extraction suits unstructured notes but incurs higher costs.

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