literature-note-formatter

Convert literature reading notes into falsifiable knowledge nodes with quantitative conditions.

Updated Aug 27, 2026
One-click install
npx skills add https://github.com/xingchen2202/obsidian-ai-knowledge-system --skill literature-note-formatter
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: literature-note-formatter
Source: https://github.com/xingchen2202/obsidian-ai-knowledge-system/tree/main/skills/literature-note-formatter
Command: npx skills add https://github.com/xingchen2202/obsidian-ai-knowledge-system --skill literature-note-formatter

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

It converts scattered literature reading into a structured, traceable research note that an AI (and you) can reason over for falsification and transferability.

Core Features & Use Cases

  • Formats existing Literature Notes: validates required frontmatter fields, checks module completeness, and fills missing sections while preserving user-provided claim content.
  • Creates Literature Notes from scratch: generates a full note from user-provided paper descriptions/abstracts without fabricating DOI/authors/zotero metadata.
  • Forces falsification-driven rigor: produces a core Claim Registry with quantifiable falsifiable conditions, a Failure Regime with numeric boundary predictions, and a Methodology Transferability scoring table.
  • Ensures research hygiene: performs citation integrity checks to prevent fabricated references and flags dangling ID links in related_to/derived_from/solves.

Quick Start

Ask the AI to format your existing literature note by converting it into a complete Literature Note with a claim registry, quantified failure regime, and methodology transferability score.

Frequently Asked Questions about literature-note-formatter

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

FAQPage Schema
How do I format literature notes for falsification and AI reasoning in Obsidian?▼

Literature note formatting converts scattered reading annotations into structured, falsification-ready knowledge nodes by enforcing required frontmatter, generating quantifiable claims, and validating reference integrity for AI reasoning.

How do I create a falsifiable claim registry from a research paper?▼

Creating a falsifiable claim registry involves extracting core claims from paper abstracts or notes, then generating at least three quantifiable falsification conditions and a numeric failure regime boundary prediction.

Can I generate Obsidian literature notes from DOIs and author names without fabricating citations?▼

Generating literature notes from bibliographic identifiers like DOIs and authors creates full knowledge nodes while strictly forbidding fabricated citations and auto-filling user-controlled fields to ensure research hygiene.

What is a numeric failure regime in research governance and when do I need it?▼

A numeric failure regime in research governance defines quantitative boundary predictions for when a falsifiable claim fails, needed when structuring literature notes for traceable methodology transferability and rigorous validation.

How do I validate citation integrity and fix dangling ID links in knowledge engineering?▼

Validating citation integrity checks reference completeness to prevent fabricated citations and flags dangling ID links in related_to, derived_from, and solves fields during literature note formatting.

Does literature note formatting preserve existing claim content when filling missing sections?▼

Formatting existing literature notes validates frontmatter, checks module completeness, and fills missing sections while strictly preserving user-provided claim content and updating note timestamps.