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Plan and create Amazon A+ Content (Enhanced Brand Content). Design module layouts, write persuasive copy, plan comparison charts, and create image briefs that convert browsers into buyers.

Premium A+ and Brand Story — module design, lifestyle imagery, comparison charts, mobile optimization

Amazon FBA Calculator - Complete fee breakdown and profit analysis

Amazon keyword research and market opportunity analysis for sellers. Retrieve autocomplete suggestions (long-tail keywords), analyze competitor landscape, and assess market opportunity for any keyword on 12 Amazon marketplaces (US/UK/DE/FR/IT/ES/JP/CA/AU/IN/MX/BR). No API key required. Make sure to use this skill whenever the user mentions Amazon product research, finding products to sell on Amazon, Amazon keyword ideas, niche analysis, competition analysis for Amazon, market opportunity on Amazon, comparing Amazon keywords, evaluating whether a product is worth selling, Amazon autocomplete data, seasonal demand for Amazon products, or anything related to researching what to sell on Amazon — even if they don't explicitly say 'keyword research'. Also trigger when the user asks vague questions like 'is this a good product to sell?', 'what's the competition like for X on Amazon?', 'should I sell X or Y?', or 'what are people searching for on Amazon?'.

Plan Amazon product photography for maximum conversion. Shot lists, lighting setups, infographic briefs, lifestyle scene planning, and image optimization following Amazon's requirements.

Guidelines for generating clinical decision support (CDS) documents: patient cohort analyses (biomarker-stratified outcomes) and treatment recommendation reports (GRADE-graded evidence). Covers document structure, executive summary design, evidence grading (GRADE 1A–2C), statistical reporting (HR, CI, survival), and biomarker integration. Use when creating pharmaceutical research documents, clinical guidelines, or regulatory submissions.

Low-level Python plotting library for full customization of scientific figures. Use for publication-quality plots (line, scatter, bar, heatmap, contour, 3D), multi-panel subplot layouts, and fine-grained control over every visual element. Export to PNG/PDF/SVG. For quick statistical plots use seaborn; for interactive plots use plotly.

PyHealth is a Python library for healthcare machine learning. Build clinical prediction models from EHR (Electronic Health Record) data: process MIMIC-III/IV, eICU, and OMOP-CDM datasets, encode medical codes (ICD, ATC, NDC), construct patient-level datasets, and train models (Transformer, RETAIN, GRASP, MedBERT) for tasks including mortality prediction, drug recommendation, readmission, and diagnosis prediction. Alternatives: FIDDLE (EHR preprocessing only), clinical-longformer (NLP on clinical notes only), ehr-ml (EHR embedding only).

Bayesian modeling with PyMC 5. 8-step workflow: define model, set priors, define likelihood, sample (NUTS/ADVI), diagnose (R-hat, ESS, divergences), interpret posteriors, compare models (LOO/WAIC), predict. Hierarchical, logistic, GP model variants. Prior/posterior predictive checks.

Classical machine learning in Python. Use for classification, regression, clustering, dimensionality reduction, model evaluation, hyperparameter tuning, and preprocessing pipelines. Covers linear models, tree ensembles, SVMs, K-Means, PCA, t-SNE. For deep learning use PyTorch/TensorFlow; for gradient boosting at scale use XGBoost/LightGBM.

Survival analysis and time-to-event modeling with scikit-survival. Cox proportional hazards (standard/elastic net), Random Survival Forests, Gradient Boosting, SVMs for censored data. C-index (Harrell/Uno), Brier score, time-dependent AUC evaluation. Kaplan-Meier, Nelson-Aalen, competing risks. scikit-learn Pipeline/GridSearchCV compatible. For frequentist regression use statsmodels; for Bayesian survival use pymc; for simpler parametric models use lifelines.

Parse and create FCS (Flow Cytometry Standard) files v2.0-3.1. Read event data as NumPy arrays, extract channel metadata, handle multi-dataset files, export to CSV/FCS. For advanced gating and compensation use FlowKit.

Query NCI Imaging Data Commons (IDC) for cancer radiology and pathology imaging datasets hosted on Google Cloud. Search DICOM collections by modality, anatomical site, cancer type, or collection name. Download images via Google Cloud Storage or IDAT tool. 50TB+ of publicly accessible DICOM images. Requires Google Cloud account for large downloads; small queries work without billing. For local DICOM processing use pydicom-medical-imaging; for whole-slide pathology use histolab.

OMERO is an open-source platform for biological image data management. Use the omero-py Python client to connect to an OMERO server, search and retrieve images as numpy arrays, annotate images with tags and key-value pairs, manage ROIs, and integrate OMERO image data into downstream analysis pipelines — all programmatically without the OMERO desktop GUI.

PathML is an open-source toolkit for computational pathology. Use it to process whole-slide images (WSIs): load slides, extract tiles, apply stain normalization and nuclear segmentation preprocessing, extract features, and train machine learning models. Supports H&E and multiplex imaging. Ideal for building end-to-end digital pathology pipelines from raw WSI files to quantitative outputs.

Pure Python DICOM library for medical imaging (CT, MRI, X-ray, ultrasound). Read/write DICOM files, extract pixel data as NumPy arrays, access/modify metadata tags, apply windowing (VOI LUT), anonymize PHI, build DICOM from scratch, process series into 3D volumes. For whole-slide pathology images use histolab; for NIfTI neuroimaging use nibabel.

Interactive scientific visualization with Plotly. Two-layer API: plotly.express (px) for one-liner DataFrame plots and plotly.graph_objects (go) for full trace-level control. 40+ chart types with hover, zoom, pan, and animation. Exports to interactive HTML or static PNG/SVG/PDF via kaleido. Use for interactive web figures, volcano plots with gene hover info, dose-response dashboards, gene expression heatmaps, and 3D molecular visualizations. Use seaborn for statistical summaries with automatic aggregation; use matplotlib for fine-grained publication figures; use plotly for interactive or web-embedded output.

Interactive visualization with Plotly. 40+ chart types (scatter, line, bar, heatmap, 3D, statistical, geographic) with hover, zoom, and pan. Use for exploratory analysis, dashboards, and presentations. Two APIs: Plotly Express (quick, DataFrame-oriented) and Graph Objects (fine-grained control). For static publication figures use matplotlib; for statistical grammar use seaborn.

Statistical visualization library built on matplotlib with native pandas DataFrame support. Automatic aggregation, confidence intervals, and grouping for distribution plots (histplot, kdeplot), categorical comparisons (boxplot, violinplot, stripplot), relational plots (scatterplot, lineplot), regression plots (regplot, lmplot), matrix plots (heatmap, clustermap), and multi-variable grids (pairplot, jointplot, FacetGrid). Use seaborn for statistical summaries with minimal code; use matplotlib for fine-grained figure control; use plotly for interactive HTML output.

Statistical visualization built on matplotlib with pandas integration. Distribution plots (histplot, kdeplot, violinplot, boxplot), relational plots (scatterplot, lineplot), categorical comparisons, regression, correlation heatmaps. Automatic aggregation and CI. For interactive plots use plotly; for low-level control use matplotlib.

Annotated data matrices for single-cell genomics. AnnData stores expression data (X) with observation metadata (obs), variable metadata (var), layers, embeddings (obsm/varm), graphs (obsp/varp), and unstructured data (uns). Use for .h5ad/.zarr file handling, dataset concatenation, and scverse ecosystem integration. For analysis workflows use scanpy; for probabilistic models use scvi-tools.

GATK Best Practices pipeline for germline SNP and indel variant calling from WGS/WES BAM files. Runs HaplotypeCaller in GVCF mode per sample, consolidates with GenomicsDBImport, joint-genotypes with GenotypeGVCFs, and applies VQSR or hard filters. Requires BWA-MEM2-aligned, markdup, and BQSR-processed BAMs. Use DeepVariant instead for a faster deep-learning alternative; GATK is the ENCODE/NIH standard for research and clinical genomics.

Geniml is a Python library for genomic interval machine learning. Train and apply region2vec embeddings to convert BED file regions into numeric vectors, load and index genomic interval datasets for ML pipelines, search embedding spaces with BEDSpace, and evaluate embedding quality. Use for chromatin accessibility clustering, regulatory element classification, cross-sample region comparison, and building ML models on genomic intervals.

NHGRI-EBI GWAS Catalog REST API for SNP-trait associations from published genome-wide association studies. Query studies, associations, variants, traits, genes, and summary statistics. Build polygenic risk score candidates, analyze variant pleiotropy, download summary statistics for Manhattan plots. No authentication required.

Ultra-fast RNA-seq transcript and gene-level quantification using quasi-mapping (no BAM required). Builds a k-mer index from a transcriptome FASTA, then quantifies reads in minutes. Outputs transcript-level TPM/count tables (quant.sf) with optional GC-bias and sequence-bias correction. Integrates directly with tximeta/tximport for DESeq2 or edgeR. Use STAR instead when a genome-aligned BAM is required for variant calling or visualization.

Python API v2 for programming Opentrons OT-2 and Flex liquid handling robots. Write protocols as Python files with metadata and a run() function; control pipettes, labware, and hardware modules (thermocycler, heater-shaker, magnetic, temperature). Simulate locally with opentrons_simulate, then upload to the robot app. Use PyLabRobot instead for hardware-agnostic scripts that run on Hamilton, Tecan, or other vendors.

Guide to quantitative Western blot analysis covering band detection, two-step normalization, fold change calculation, statistical aggregation across biological replicates, and publication-ready visualization. Consult when analyzing blot images with multiple conditions and repetitions, choosing normalization strategies, or preparing densitometry figures for publication.

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Pipeline for analyzing Neuropixels extracellular electrophysiology recordings. Covers probe geometry loading (ProbeInterface), spike sorting with Kilosort via SpikeInterface, quality metrics computation, unit curation (ISI violations, firing rate, signal-to-noise), and post-sort analysis (PSTH, tuning curves, population decoding) using pandas and matplotlib. Designed for acute and chronic Neuropixels 1.0/2.0/Ultra recordings from rodent and primate experiments.

PyTorch Geometric (PyG) for graph neural networks. Node classification, graph classification, link prediction with GCN, GAT, GraphSAGE, GIN layers. Message passing framework, mini-batch processing, heterogeneous graphs, neighbor sampling for large-scale learning, model explainability. Supports molecular property prediction (QM9, MoleculeNet), social networks, knowledge graphs, 3D point clouds. For non-graph deep learning use PyTorch directly; for traditional graph algorithms use NetworkX.

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Access US Patent and Trademark Office (USPTO) patent data via the PatentsView REST API and Google Patents Public Data (BigQuery). Use it to search patents by inventor, assignee, CPC classification, or keywords; download full patent metadata and claims; analyze patent portfolios; and track technology trends. Ideal for IP landscape analysis, competitor monitoring, prior art searches, and technology forecasting in life sciences and biotech.

Guide for designing and creating scientific schematics, diagrams, and graphical abstracts. Covers tool selection (BioRender, Inkscape, Affinity Designer, PowerPoint), design principles for biological pathway diagrams, molecular mechanism schematics, experimental workflow diagrams, and graphical abstracts for journal submissions. Includes composition rules, icon sourcing, color usage for biological entities, and accessibility considerations. Use when planning or creating a scientific figure that is primarily illustrative rather than data-driven.

Create effective scientific presentations for conferences, seminars, thesis defenses, and grant pitches. Slide design principles, talk structure, timing, data visualization for slides, quality assurance. Works with PowerPoint and LaTeX Beamer. For poster creation use latex-research-posters.

Search and retrieve cryo-EM density maps, fitted atomic models, and metadata from the Electron Microscopy Data Bank (EMDB) REST API. Query by keyword, resolution, method, or organism; fetch entry details, map download URLs, associated PDB models, and publications. No authentication required. For experimental atomic coordinates use pdb-database; for AlphaFold predicted structures use alphafold-database-access.

Python library for analyzing molecular dynamics (MD) trajectories from GROMACS, AMBER, NAMD, CHARMM, and LAMMPS. Reads topology and trajectory files into Universe objects; supports RMSD, RMSF, radius of gyration, contact maps, hydrogen bond analysis, PCA, and custom distance/angle calculations across millions of frames. Use for structural analysis after MD simulations; use OpenMM or GROMACS directly for running simulations.

Molecular featurization hub (100+ featurizers) for ML. Convert SMILES to numerical representations via fingerprints (ECFP, MACCS, MAP4), descriptors (RDKit 2D, Mordred), pretrained models (ChemBERTa, GIN, Graphormer), and pharmacophore features. Scikit-learn compatible transformers with parallelization, caching, and state persistence. For QSAR, virtual screening, similarity search, and deep learning on molecules.

Query Open Targets Platform GraphQL API for target-disease associations, evidence scores, drug-target links, and safety data. Search targets by gene symbol, diseases by EFO ID, retrieve evidence scores from 20+ data sources, drug mechanisms, and tractability assessments. For ChEMBL bioactivity use chembl-database-bioactivity; for clinical trials use clinicaltrials-database-search.

Query RCSB PDB (200K+ experimental structures) via rcsb-api Python SDK. Text, attribute, sequence, and structure similarity search. Fetch metadata via Schema or GraphQL. Download PDB/mmCIF coordinate files. For AlphaFold predicted structures use alphafold-database-access.