Lets Claude drive the pyzotero client to search, create, update and export your Zotero reference library.
Guides Claude to build, simulate, transpile, and run quantum circuits with modern Qiskit 2.x and IBM Quantum Runtime.
Run a complete bulk RNA-seq differential expression analysis in Python with PyDESeq2, from raw counts to volcano plots.
Guides correct pysam usage for reading, querying, filtering, and writing SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data.
Teaches Claude to build and run cloud molecular-modeling pipelines — pKa, conformers, docking, cofolding — through Rowan's Python API.
Describe a scientific diagram in plain language and get a publication-oriented PNG that an AI reviewer scores against your document type's quality bar.
Gives Claude fine-grained RDKit guidance for SMILES/SDF parsing, descriptors, fingerprints, substructure search, reactions and 2D/3D generation.
An end-to-end Scanpy workflow skill for scRNA-seq: QC, normalization, dimensionality reduction, clustering, marker genes and cell-type annotation.
Writes competitive research proposals tailored to NSF, NIH, DOE, DARPA, and Taiwan NSTC review criteria, formats, and budget rules.
Zero-shot time series forecasting with Google's TimesFM foundation model — no training required.
Qualitative-first, evidence-traceable developmental review for papers, protocols and research ideas.
A complete scVelo workflow skill for inferring cell-state transition directions, latent time, and driver genes from spliced/unspliced mRNA dynamics.
Guides the full Stable Baselines3 workflow — algorithm choice, custom envs, callbacks, training and evaluation.
Systematically evaluates research claims for design flaws, bias, statistical errors and evidence quality using GRADE and Cochrane Risk of Bias.
Equips Claude to run scikit-bio workflows — sequences, alignments, phylogenetic trees, diversity metrics, PCoA and PERMANOVA — with the current 0.7+ API.
A reference skill that makes Claude write modern, correct seaborn 0.13 code for distributions, relationships and categorical comparisons from pandas DataFrames.
An expert-level skill for building, testing, and analyzing bounded process-based discrete-event simulations with SimPy.
Guides Claude through rigorous statsmodels work — OLS, GLM, discrete choice, and time series — with full diagnostics and inference.
Gives Claude accurate, version-current PyTorch Geometric patterns for node/link/graph tasks, heterogeneous graphs, and large-scale sampling.
Writes and debugs TorchDrug 0.2.1 code for molecular property prediction, generation, retrosynthesis, protein and knowledge-graph learning.