literature-search

Search academic papers and extract experimental results via citation graph traversal.

150|19|Updated Jun 15, 2026

Search Tags:#citations #academic research #literature search #source code #paper analysis

Ready to run

npx skills add https://github.com/gaasher/Agent-Loop-Skills --skill literature-search-gaasher

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: literature-search
Source: https://github.com/gaasher/Agent-Loop-Skills/tree/main/loops/literature-search
Command: npx skills add https://github.com/gaasher/Agent-Loop-Skills --skill literature-search-gaasher

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires python>=3.9, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill solves the problem of scholarly research by providing robust literature retrieval and analysis capabilities, enhancing the efficiency and depth of research loops.

Core Features & Use Cases

Literature Retrieval: Offers a shared toolchain for paper discovery, novelty checks, and full-text snippet search across multiple sources.
Citation Graph Traversal: Allows users to traverse the citation graph for a paper, exploring its references and citations.
Experimental Result Extraction: Extracts structured experimental results from papers, aiding in evidence grading.
Use Case: For a researcher working on a project, this Skill can be used to automatically search for relevant papers, extract key information, and analyze experimental results, significantly reducing the time spent on manual literature review.

Quick Start

Use the literature-search skill to search for papers on "machine learning" and extract their citations.

Frequently Asked Questions about literature-search

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

FAQPage Schema

How do I automate academic literature search and extract citations for a research review?▼

You can automate academic literature search and extract citations by using semantic search and citation graph traversal. This toolchain retrieves relevant papers, explores their references, and extracts structured experimental results to enhance scholarly research loops.

How does citation graph traversal work for discovering related academic papers?▼

Citation graph traversal works by mapping the references and citations of a specific paper. It allows researchers to navigate forward and backward through academic literature networks, systematically discovering related papers and analyzing their experimental results.

Can I perform a full-text snippet search across multiple academic sources using Python?▼

Yes, you can perform full-text snippet search across multiple academic sources using Python 3.9 or higher. The toolchain integrates various APIs to retrieve snippets, check novelty, and extract structured experimental data for evidence grading.

What is the best way to extract structured experimental results from research papers?▼

The best way to extract structured experimental results is by using an automated paper analysis toolchain. It processes retrieved literature to isolate experimental data, aiding directly in evidence grading and reducing manual literature review time.

Do I need Python to run this literature retrieval and analysis toolchain?▼

Yes, you need Python 3.9 or higher to run this literature retrieval and analysis toolchain. The environment executes scripts that interact with various external APIs for semantic search, snippet search, and citation graph traversal.

Are there limitations when using external APIs for paper discovery and novelty checks?▼

Yes, limitations exist when using external APIs for paper discovery and novelty checks. Data retrieval depends on API availability and rate limits, which can impact the depth of full-text snippet search and citation graph traversal during your research workflow.