medical-research-skills

Fetch and save the original HTML of scientific literature webpages when given a URL, DOI, or PubMed PMID (triggered when you need archival-grade page HTML for downstream parsing or review).

Analyze funding abstracts and project metadata to identify topic shifts and forecast near-term grant priorities.

Query the NCBI Gene database via E-utilities and the NCBI Datasets API; use it when you need to search genes by symbol/ID and retrieve annotations (RefSeq, GO, location, phenotype) for single or batch gene lists.

Retrieves comprehensive gene information including PubMed publication counts, NCBI summaries, and Ensembl transcript data. Supports batch processing and file input. Invoke when the user asks for gene details, publication statistics, or needs to analyze a list of genes.

Use when normalizing bulk gene or protein expression matrices with log2 transform, z-score standardization, or min-max scaling before downstream visualization or exploratory analysis. NOT for count-model normalization such as TPM/DESeq2 size factors, batch correction, or single-cell preprocessing.

Generates complete phenotype-scoring bioinformatics research designs for any disease context and any user-defined phenotype, pathway, process, signature, or molecular program. Use when a study centers on gene-set or feature-set definition, intersection with DEGs or candidate features, phenotype scoring, feature selection, diagnostic or stratification assessment, immune or cellular-resolution interpretation, network analysis, and optional orthogonal validation. Covers five study patterns (signature discovery, phenotype scoring, feature selection, immune/cellular interpretation, multi-layer validation) and always outputs Lite / Standard / Advanced / Publication+ with a recommended primary plan, stepwise workflow, figure plan, validation hierarchy, minimal executable version, publication upgrade path, and strictly verified literature retrieval.

Use gene structure mapper for data analysis workflows that need structured execution, explicit assumptions, and clear output boundaries.

Machine learning toolkit for genomic interval (BED) data; use it when you need to tokenize BED collections and train embeddings for regions/cells/labels, build consensus peak universes, or run similarity search and downstream ML on chromatin accessibility datasets.

A Python library for reading, writing, and analyzing geospatial vector data; use it when you need spatial operations (buffer/overlay/join), CRS reprojection, or map visualization on formats like Shapefile/GeoJSON/GeoPackage or PostGIS.

Search for gene expression DataSets and Profiles in the NCBI GEO database. Use this skill when the user wants to find microarray, RNA-seq, or other genomic data by keywords, organism, author, or specific fields.

Unified CLI/Python interface for querying genomic, proteomic, structure, and expression data across 20+ bioinformatics databases; use when you need fast, scriptable retrieval by gene/protein IDs or keywords.

Use when performing GO and KEGG enrichment on a gene list from bulk RNA-seq or microarray studies, then generating a combined GO/KEGG dot chart. NOT for single-cell RNA-seq, methylation data, or non-expression data.

Use grant budget justification for academic writing workflows that need structured execution, explicit assumptions, and clear output boundaries.

NIH funding trend analysis to identify high-priority research areas.

Use grant gantt chart gen for evidence insight workflows that need structured execution, explicit assumptions, and clear output boundaries.

Simulate structured grant peer review for biomedical proposals; use when stress-testing significance, innovation, approach, feasibility, and reviewer-facing weaknesses before submission.

Assist with biomedical grant proposal drafting, structure, and revision; use when preparing fundable proposal sections, aligning aims and methods, or improving reviewer-facing clarity.

Writes Specific Aims pages for grant applications. Use when drafting or revising the Specific Aims page (NIH R01/R21/R03), NSF Project Summary, or equivalent for any major funding agency. Also triggers on "write my specific aims", "help me draft specific aims for NIH", "what should a specific aims page include", "NSF project summary", "write my grant aims", or "how do I structure an R01".

Converts a biomedical study storyline into a graphical abstract and, when direct image capability is available, generates the graphical abstract directly; otherwise it falls back to prompts, Mermaid flowcharts, or designer-facing briefs.

Generate graphical abstract layout recommendations based on paper abstracts.

Use when interpreting scientific graphs and charts, explaining data visualizations for research presentations, writing figure captions for publications, or analyzing trends in clinical research data. Converts complex visual data into clear, accurate explanations for academic papers, clinical reports, and public presentations.

Run GSEA on a ranked gene list and produce the enrichment table, running-score table, and enrichment plots.

Use this skill to run GSVA or ssGSEA pathway-level differential analysis from a bulk expression matrix and a sample group file, then generate a heatmap from the saved GSVA result object. Trigger keywords: GSVA, ssGSEA, pathway enrichment, KEGG pathway analysis, MSigDB. NOT for: gene-level differential expression, single-cell analysis, methylation analysis, clinical diagnosis.

A high-performance Rust toolkit (with Python bindings and a CLI) for genomic interval analysis; use it when you need fast overlap queries, coverage track generation, genomic tokenization for ML, reference sequence verification, or fragment processing.

Query NHGRI-EBI GWAS Catalog for SNP-trait associations. Search variants by rs ID, disease/trait, gene, retrieve p-values and summary statistics, for genetic epidemiology and polygenic risk scores.