Academic Research Skipv1.0.1

A PhD student uses the academic research skill to automate literature reviews on transformer architectures by executing a multi-step script that searches, deduplicates, and synthesizes 30 papers into a structured markdown report, saving weeks of manual work.

A corporate R&D team searches for papers by key authors and citation chains to map innovation trends in CRISPR technology, identifying competitor activity and potential collaboration opportunities via open access full texts.

A university grant writer gathers recent publications on algorithmic literacy in education, using year filters and citation sorting to compile a comprehensive reference list and thematic clusters for a funding proposal.

A science journalist looks up a specific DOI to retrieve metadata (authors, abstract, citation count) for fact-checking a news story, then fetches the open access URL to verify claims directly from the source.

An instructional designer searches for the most-cited papers on deep learning architectures and downloads their full texts via the deep read feature to develop up-to-date training materials for engineers.

Integrate the academic search and literature review scripts into a web-based platform where researchers pay a monthly subscription for unlimited automated literature reviews and paper discovery.

Expose the OpenAlex-based functionality as a public API with a free tier for basic searches and a paid tier for high-volume usage, deep reads, and citation chain analysis, targeting universities and small labs.

Offer a managed service where clients (e.g., corporate R&D, policy institutes) submit topics and receive custom, peer-reviewed literature reviews produced by running the automation scripts and then human-curated by experts.