Research Paper Knowledge Base

Research Paper Knowledge Base

Build and maintain a persistent, structured knowledge base from academic papers that persists across sessions. This skill enables cumulative literature understanding by storing extracted insights, cross-references, and analytical notes in a queryable format that grows with each reading session.

Overview

A core challenge in literature review work is that insights from individual papers are often lost between reading sessions. Researchers read a paper, extract key findings, then move on -- only to forget critical details weeks later when writing their own manuscript or encountering a related paper. Traditional reference managers store metadata and PDFs but do not capture the analytical work of reading: the connections between papers, the critiques of methodology, the synthesis of findings across studies.

This skill creates a structured knowledge base that captures not just what papers say, but how they relate to each other and to the researcher's own questions. Each paper entry includes standard metadata, section-by-section notes, methodological assessments, extracted claims with evidence quality ratings, and explicit connections to other papers in the knowledge base.

The knowledge base is stored in a human-readable format (Markdown + YAML frontmatter) that can be version-controlled with git, searched with standard tools, and read by both humans and AI assistants. When returning to the literature after days or weeks, the researcher (or their AI assistant) can query the knowledge base to recall prior findings, identify gaps, and build on accumulated understanding.

Knowledge Base Structure

Directory Layout

research-kb/
  _index.yaml              # Master index of all papers
  _themes.yaml             # Cross-cutting themes and concepts
  _questions.yaml           # Active research questions
  papers/
    smith-2024-deep-learning-proteins/
      notes.md              # Structured paper notes
      claims.yaml           # Extracted claims with evidence
      figures/              # Saved key figures (optional)
    jones-2023-attention-mechanisms/
      notes.md
      claims.yaml
  syntheses/
    attention-in-biology.md  # Cross-paper synthesis documents
    methodology-comparison.md

Paper Notes Template

---
paper_id: smith-2024-deep-learning-proteins
title: "Deep Learning for Protein Structure Prediction: A Survey"