Extract problem, method, and results from research paper PDFs.
npx skills add https://github.com/gabfssilva/scimesh --skill extracting
Please help me install this Agent Skill. Skill: extracting Source: https://github.com/gabfssilva/scimesh/tree/main/skills/extracting Command: npx skills add https://github.com/gabfssilva/scimesh --skill extracting
This Skill automates the process of extracting key evidence (problem, method, results) from research papers, significantly speeding up the systematic literature review process.
Launch the evidence extraction process for all included papers that have a PDF available.
High-intent search queries and answers about installing and using this skill.
Automated evidence extraction from research papers is handled by parallel sub-agents that condense the problem, method, and results from PDFs. This process applies structured frontmatter tagging based on your review protocol to organize extracted data efficiently.
Extracting problems, methods, and results from multiple PDFs concurrently is achieved using specialized parallel sub-agents. This automated condensing process structures the extracted evidence with frontmatter and tags within a structured review directory.
Processing research papers without an available PDF is supported by the extraction workflow. The system handles papers both with and without PDFs, organizing whatever evidence is available within your structured review directory.
Tagging and organizing extracted paper data according to a review protocol is done automatically by adding structured frontmatter with relevant tags. This prepares the condensed problem, method, and results data directly for final synthesis.
The literature review extraction process operates without external dependencies, utilizing internal scripts and references. It independently manages the parallel sub-agents and frontmatter tagging required for evidence condensing.
Automating systematic review evidence condensing from research papers is limited by the availability of source PDFs, although papers without PDFs are still handled. The depth of extraction relies entirely on the content provided within the included research documents.
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