Protein structure retrieval from RCSB PDB, PDBe, AlphaFold, SWISS-MODEL, 3D-Beacons (cross-provider structure aggregator), and PDB-REDO (re-refined X-ray structures), with disambiguation, quality assessment (resolution, R-factor, pLDDT, coverage), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs homology-model vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, batch model-availability checks across a gene list, checking every structure provider at once, and selecting structures for drug design or modeling.

Cross-species gene comparison and ortholog analysis. Integrates Ensembl Compara orthologs, NCBI Gene, UniProt, OLS, Monarch, and OpenTargets to identify orthologs, paralogs, sequence conservation, functional conservation across species, and lineage-specific gene gains/losses. Use for phylogenetic gene tracing, model-organism mapping, and evolutionary-genomics queries.

Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, BindingDB ligand-affinity data, BMRB NMR data, CATH/InterPro fold classification, intrinsic-disorder prediction (MobiDB/DisProt/IUPred3), membrane-protein topology (OPM/TopDB/PDBTM/ChannelsDB), enzyme catalytic-site data (M-CSA), PDBe ligand/compound chemistry, and MD simulation trajectory metadata (MDDB). Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), antibody-target interface analysis, disordered-region assessment, membrane-protein topology, catalytic-mechanism lookup, and finding published MD simulations for a target.

Single-cell RNA-seq analysis with scanpy/anndata — h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.

Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation by nonlinear regression (and reports Lineweaver-Burk for reference). Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor. NOT for BRENDA database lookups of published constants (use the BRENDA tools).

Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target before commit-to-medchem, target prioritization across a list, and target-deselection rationale.

Aging biology, cellular senescence, and longevity research. Covers senescence markers (p16/CDKN2A, SASP, SA-beta-gal), aging hallmarks, senolytic drug discovery (dasatinib+quercetin, fisetin, navitoclax), epigenetic clocks, telomere biology, and longevity GWAS. Use for senescence-pathway analysis, age-related disease genetics, senolytic-target discovery, and centenarian-genetics queries. Distinguishes correlative vs causal evidence (knockout, intervention).

Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.

Build a weekly cadence of customer touchpoints using Opportunity Solution Trees, assumption mapping, and interview snapshots. Use when the user mentions "continuous discovery", "opportunity solution tree", "weekly interviews", "assumption testing", "discovery habits", "product trio", "outcome-based roadmap", "how do I talk to customers regularly", "we keep building things nobody uses", or "connect research to the roadmap". Also trigger when setting up regular customer feedback loops, prioritizing which experiments to run, or tying discovery insights to delivery work. Covers experience mapping, co-creation, and prioritizing opportunities. For interview technique, see mom-test. For team structure, see inspired-product.

Create or update reusable iPolloWork templates through conversation for Design, Slides/native editable PPT, and HyperFrames Video. Use when planning, generating, repairing, validating, or saving a template package, manifest, reusable variables, cover, slide/PPT markers, video composition, or design-system token integration.

Prepare journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Use when selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.

Prepare journal manuscripts, conference papers, research posters, and grant documents using venue-specific formatting guidance and bundled LaTeX scaffolds. Use when selecting an official template, checking current page or anonymity rules, adapting academic writing to a venue, or inspecting a submission PDF.

Find bugs, security vulnerabilities, and code quality issues in local branch changes. Use when asked to review changes, find bugs, security review, or audit code on the current branch.

Inspect Open-Science's JavaScript control REPL, discover managed Project files, Sessions, and Agent Frames, and safely feature-gate host.* calls with host.capabilities(). Use when an Agent needs to discover available host APIs, locate an Artifact or Upload Version, diagnose a Session, or read a Frame transcript in the current Project.

Use when performing time-dependent ROC curve analysis for survival data with follow-up time, event status, and a numeric marker. Supports CSV/TXT/TSV/Excel input, `risk_score` as the default marker unless `--marker_col` is provided, parameter validation, standardized output directories, AUC table export, ROC point export, and PDF figure generation.

Use when screening differentially expressed genes from a bulk expression matrix between two user-specified groups, producing DEG tables, a volcano plot, and a clustered heatmap. Triggers include DEG analysis, volcano plot, clustered heatmap, limma-based two-group comparison, and case-vs-control screening. NOT for single-cell RNA-seq, multi-group contrasts, count-model workflows such as DESeq2/edgeR, or non-expression omics data.

Generates complete dual-disease transcriptomic + machine learning research designs from a user-provided disease pair. Use when users want to identify shared DEGs, common hub genes, cross-disease biomarkers, or shared molecular mechanisms between two diseases using public GEO data. Triggers:"shared biomarker study for two diseases", "dual-disease transcriptomic ML paper", "identify common DEGs between disease A and B", "cross-disease hub gene discovery", "shared DEG + PPI + ROC design", "immune infiltration shared biomarker", or "I want to study disease X and Y together". Always outputs four workload configurations (Lite / Standard / Advanced / Publication+) with a recommended primary plan, step-by-step workflow, figure plan, validation strategy, minimal executable version, and publication upgrade path.

Use when performing correlation analysis between two variables including Pearson and Spearman correlation methods. Supports command-line parameter input, automatic data format detection, parameter validation, result directory creation, and CSV or TXT format result export.

Converts an audited medical research gap into a complete, structured, gap-traceable study design. Always use this skill whenever a user already has one or more candidate research gaps and wants to transform them into an executable biomedical research plan rather than re-run broad topic ideation. Covers six gap-to-design patterns (evidence-completion, mechanism-resolution, cell-state/context-mapping, translation-bridge, causality-upgrade, population/stage-specific) and always outputs one recommended primary protocol, a gap-to-design dependency map, step-by-step workflow, figure plan, validation strategy, minimal executable version, publication upgrade path, and verified design-support literature rules. Never fabricate references. Preserve claim-evidence discipline and do not replace a topic-specific gap with a generic workflow.

Use when building XGBoost models on tabular data and returning feature importance ranking outputs. Supports binary classification and regression with automatic task detection, train-test split, performance tables, feature importance ranking tables, and PNG importance plots.