Optimize medical AI papers for AI search engines and RAG tools.
npx skills add https://github.com/Aperivue/medsci-skills --skill academic-aio
Please help me install this Agent Skill. Skill: academic-aio Source: https://github.com/Aperivue/medsci-skills/tree/main/skills/academic-aio Command: npx skills add https://github.com/Aperivue/medsci-skills --skill academic-aio
Medical researchers often struggle to surface and correctly index AI-focused papers for AI search engines, LLMs, and RAG tools. This skill provides structured guidance and metadata templates to improve discoverability, reproducibility, and citation integrity of medical-AI manuscripts, README metadata, and model/dataset cards.
Create a metadata skeleton for your medical-AI manuscript and request an audit of discoverability.
High-intent search queries and answers about installing and using this skill.
Optimize medical AI papers by applying structured metadata templates and alignment frameworks like TRIPOD+AI and CLAIM to enhance discoverability for AI search engines. This generates indexing-friendly summaries and actionable checklists for titles, abstracts, and model cards.
Formatting a manuscript for Perplexity and ChatGPT web indexing involves creating structured summaries like Key Points and Plain-Language Summaries. This ensures your medical AI paper surfaces accurately in RAG-based tools and AI search results.
Yes, it supports TRIPOD+AI, STARD-AI, TRIPOD-LLM, and DECIDE-AI guidance. These frameworks are applied to align manuscripts, preprints, and model cards with established medical AI reporting standards for accurate attribution.
Yes, you can generate metadata surfaces for GitHub READMEs, CITATION.cff entries, Zenodo, and Hugging Face model or dataset cards. It produces metadata-ready outputs to maximize discoverability and reproducibility across these platforms.
Improve preprint discoverability on medRxiv and arXiv by applying GEO principles and structured metadata templates. This enhances how AI search engines index your medical AI manuscripts and improves citation integrity.
Limitations depend on the target journal's specific formatting requirements for titles and abstracts. While it provides reusable frameworks for Lancet Digital Health and Nature Medicine, manual review remains necessary to ensure strict editorial compliance.
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