Browse reusable Agent Skills, each with a clear purpose and practical guidance.
Recommend academic journals based on manuscript topic, abstract, and impact factor expectations. Use when the user wants to find suitable journals for their research manuscript, especially when they provide a topic, abstract, and target Impact Factor.
Recommends target journals for manuscript submission by analyzing the paper topic/abstract and the journal distribution of similar PubMed literature; use when users ask for journal recommendation/matching, submission strategy, PubMed search, or similar-literature statistics.
Direct access to KEGG via the REST API for academic-only pathway/gene/compound/drug queries; use when you need precise HTTP-level control or targeted KEGG ID mapping.
Calculate literature growth velocity and acceleration to assess research.
Search and locate relevant content within a local knowledge base (files, indices, or PageIndex). Use when you need verifiable citations (file + page/paragraph) to support answers from local sources.
Filter literature by publication year, journal, and predefined screening rules to produce inclusion/exclusion lists; use when conducting preliminary screening or systematic review screening to narrow the literature scope.
Use this bioinformatics data analysis skill to construct a database-driven lncRNA-mRNA regulatory network from target lncRNA and/or gene lists by projecting shared miRNA evidence from local ceRNA reference tables. It does not infer networks from expression matrices.
Extends a mechanistic or association-level biomedical finding into a staged validation pathway that moves from descriptive evidence toward stronger functional support, mechanistic specificity, and clinical relevance. Use this skill when a user has a pathway, biomarker, cell-state, target, mechanism, or association finding and needs to decide what should be validated next, in what order, and which evidence layers are necessary versus optional. Do not default to maximal validation stacks. Build a structured validation ladder with a primary route, stronger upgrade route, and optional extensions.
Matches a user’s biomedical research direction, disease problem, study aim, data modality, and resource constraints to the most relevant recent algorithms and method papers. Always search real recent algorithm literature first, prioritize the last 12 months, expand to 1–3 years only when needed, and add canonical baselines only when necessary. Every formal algorithm recommendation must include the verified primary method paper, plus published downstream papers that actually cite/use the algorithm when such papers are found, with DOI when available. Never fabricate papers, algorithm names, authors, journals, years, DOI, PMID, links, or benchmark claims. If no directly verified algorithm paper is found, say so explicitly.