Convert structured paper JSON data into a research gap matrix with CSV output.
npx skills add https://github.com/Eureka39/CARS --skill matrix-engine
Please help me install this Agent Skill. Skill: matrix-engine Source: https://github.com/Eureka39/CARS/tree/main/modules/matrix-engine Command: npx skills add https://github.com/Eureka39/CARS --skill matrix-engine
This Skill addresses the challenge of identifying and visualizing research gaps by constructing a research landscape from structured paper data.
Generate the research landscape matrix for the 'ProjectX' hypothesis set by executing the 'generate matrix' command.
High-intent search queries and answers about installing and using this skill.
Constructing a research landscape matrix from structured paper data identifies research gaps by normalizing labels and mapping them onto a 2D chessboard, automatically highlighting uncovered cells as potential areas for further academic exploration.
A research landscape matrix is a 2D chessboard visualization built from structured paper JSONs that represents the current state of academic research, making it easier to systematically identify and analyze uncovered research gaps across different variables.
You generate a research landscape matrix by processing structured JSON paper data, which normalizes labels, identifies gaps, and outputs both a machine-readable JSON file and a human-readable CSV file for further analysis.
Yes, the research gap identification process requires reading and processing JSON structured data containing paper information, which is then normalized and mapped into a matrix to output both JSON and CSV files.
Converting structured paper data into a research landscape yields a machine-readable JSON file containing the structured matrix data and a human-readable CSV file detailing the identified research gaps for easy review.
Yes, you can input structured paper data exported from the Literature Reader module to construct a 2D research landscape matrix, allowing you to visualize and systematically identify specific research gaps for future exploration.
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