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Expert sinusitis consultant. IMMEDIATELY search 10+ authoritative sources (UpToDate, AAO-HNS, EPOS, Mayo, PubMed/PMC, Cleveland Clinic, Hopkins, Springer, NORD, ENT Today, USA Sinus) for ANY sinus question. Use WebFetch to read sources deeply. Mark uncertainty explicitly.
Converts neurophysiology data to NWB format with automatic format detection and intelligent error handling
Work with raster and imagery data including ImageryLayer, ImageryTileLayer, multidimensional data, pixel filtering, and raster analysis. Use for satellite imagery, elevation data, and scientific raster datasets.
Form infrastructure for Astro. Zod validation, email, rate limiting, Turnstile, GDPR, Sheets. FAIL = no conversion.
Work with coordinate systems, projections, and coordinate conversion. Use for transforming coordinates and displaying position in multiple formats.
Cloud-based quantum chemistry platform with Python API. Preferred for computational chemistry workflows including pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2). Use when tasks involve quantum chemistry calculations, molecular property prediction, DFT or semiempirical methods, neural network potentials (AIMNet2), protein-ligand binding predictions, or automated computational chemistry pipelines. Provides cloud compute resources with no local setup required.
Molecular ML with diverse featurizers and pre-built datasets. Use for property prediction (ADMET, toxicity) with traditional ML or GNNs when you want extensive featurization options and MoleculeNet benchmarks. Best for quick experiments with pre-trained models, diverse molecular representations. For graph-first PyTorch workflows use torchdrug; for benchmark datasets use pytdc.
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
Access AlphaFold 200M+ AI-predicted protein structures. Retrieve structures by UniProt ID, download PDB/mmCIF files, analyze confidence metrics (pLDDT, PAE), for drug discovery and structural biology.
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with uncertainty, or multi-modal integration (TOTALVI, MultiVI). Best for advanced modeling, batch effects, multimodal data. For standard analysis pipelines use scanpy.
Modern RDKit workflows for cheminformatics, including molecular fingerprints, drawing, and property calculations. Use when working with molecules, SMILES, molecular fingerprints (Morgan, ECFP, RDKit, atom pairs, topological torsions), molecule visualization/drawing, substructure search, or chemical property calculations. This skill provides up-to-date syntax patterns as RDKit's API evolves.
Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
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.
"Hierarchical task decomposition with control flow nodes and dual memory systems from ReAcTree research. Trigger keywords: ReAcTree, parallel execution, working memory, episodic memory, LOOP, CONDITIONAL, FEEDBACK, hierarchical agents, coordination, control flow, agent trees"
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
