Generate deterministic Markdown summaries from PDFs with evidence anchors.
npx skills add https://github.com/jimezsa/opencolab --skill paper-summary-jimezsa
Please help me install this Agent Skill. Skill: paper-summary Source: https://github.com/jimezsa/opencolab/tree/main/projects/SKILLS/paper-summary Command: npx skills add https://github.com/jimezsa/opencolab --skill paper-summary-jimezsa
Automatically generate deterministic, schema-conformant Markdown summaries from research PDFs to accelerate literature review and indexing.
Provide a PDF path or a directory of PDFs and run the paper-summary workflow to generate schema-conformant Markdown summaries.
High-intent search queries and answers about installing and using this skill.
Generating markdown summaries from PDFs involves providing a single file or directory path to the workflow. The tool reads the PDFs and outputs schema-conformant markdown summaries directly next to the source files.
Yes, you can batch process research PDFs by providing a directory path. The workflow generates deterministic markdown summaries for each paper to streamline literature indexing.
Yes, the PDF summarization workflow preserves evidence anchors from the source document. It retains figures, captions, and tables from the source PDFs to ensure traceability during literature review.
Yes, generating markdown paper summaries requires Python 3 and the google-genai client. These dependencies are necessary to run the workflow and process the PDF files into deterministic markdown.
Yes, you can use metadata JSONs to enrich markdown summaries generated from PDFs. The workflow reads optional metadata files to integrate additional context into the final markdown output.
The best way to convert research PDFs to deterministic markdown is using a schema-conformant workflow that leverages Gemini. This approach ensures consistent summaries while keeping source evidence anchors intact.
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