Extract toxicity and peptide data from open-access literature via Europe PMC.
npx skills add https://github.com/cihebi2/codex_skills --skill tox-paper-extractor
Please help me install this Agent Skill. Skill: tox-paper-extractor Source: https://github.com/cihebi2/codex_skills/tree/main/tox-paper-extractor Command: npx skills add https://github.com/cihebi2/codex_skills --skill tox-paper-extractor
This Skill automates an end-to-end open-access (OA) literature workflow for toxicity/peptide data, from candidate discovery to downloaded OA full texts and auditable extractions, enabling faster, repeatable data gathering for training and analysis.
Run the tox-paper-extractor on a set of publications using the provided pipeline script, for example:
High-intent search queries and answers about installing and using this skill.
You can automate toxicity data extraction by running a pipeline script that queries Europe PMC, downloads OA full texts, and outputs auditable CSV files and extracted tables. This streamlines gathering toxicity and peptide endpoint data for analysis.
To extract experimental records from publications, a deterministic pipeline processes downloaded JATS XML and PDF files, parsing the content into raw_extractions.csv, raw_experimental_records.csv, and structured extracted_tables for reproducible data gathering.
Yes, you can discover and download peptide toxicity papers from Europe PMC by passing a search query to the pipeline script. It handles candidate discovery, retrieves OA full texts, and screens the literature for training readiness.
The best way to prepare toxicity literature for model training is using a pipeline that flags sequences, units, and censoring, generating train_ready_strict and train_ready_relaxed outputs to ensure your extracted datasets meet structured readiness criteria.
Yes, you need pandas, requests, and PyMuPDF installed, as the pipeline relies on these dependencies to handle API calls, process PDF full texts, and structure the extracted toxicity data into auditable CSV outputs.
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