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Agent skills
ai-scientist

Topic: ai-scientist

199 skills in this topic.

pymatgen Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.
K-Dense-AI/claude-scientific-skills 16,890
pymc Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
K-Dense-AI/claude-scientific-skills 16,890
statistical-analysis Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
K-Dense-AI/claude-scientific-skills 16,890
anndata Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
K-Dense-AI/claude-scientific-skills 16,890
qiskit IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.
K-Dense-AI/claude-scientific-skills 16,890
gtars High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
K-Dense-AI/claude-scientific-skills 16,890
diffdock Diffusion-based molecular docking. Predict protein-ligand binding poses from PDB/SMILES, confidence scores, virtual screening, for structure-based drug design. Not for affinity prediction.
K-Dense-AI/claude-scientific-skills 16,890
matplotlib Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
K-Dense-AI/claude-scientific-skills 16,890
medchem Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
K-Dense-AI/claude-scientific-skills 16,890
scikit-learn Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
K-Dense-AI/claude-scientific-skills 16,890
geomaster Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
K-Dense-AI/claude-scientific-skills 16,890
scientific-brainstorming Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.
K-Dense-AI/claude-scientific-skills 16,890
usfiscaldata Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
K-Dense-AI/claude-scientific-skills 16,890
neuropixels-analysis 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.
K-Dense-AI/claude-scientific-skills 16,890
docx Use this skill whenever the user wants to create, read, edit, or manipulate Word documents (.docx files). Triggers include: any mention of 'Word doc', 'word document', '.docx', or requests to produce professional documents with formatting like tables of contents, headings, page numbers, or letterheads. Also use when extracting or reorganizing content from .docx files, inserting or replacing images in documents, performing find-and-replace in Word files, working with tracked changes or comments, or converting content into a polished Word document. If the user asks for a 'report', 'memo', 'letter', 'template', or similar deliverable as a Word or .docx file, use this skill. Do NOT use for PDFs, spreadsheets, Google Docs, or general coding tasks unrelated to document generation.
K-Dense-AI/claude-scientific-skills 16,890
dnanexus-integration DNAnexus cloud genomics platform. Build apps/applets, manage data (upload/download), dxpy Python SDK, run workflows, FASTQ/BAM/VCF, for genomics pipeline development and execution.
K-Dense-AI/claude-scientific-skills 16,890
latchbio-integration Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
K-Dense-AI/claude-scientific-skills 16,890
scholar-evaluation Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
K-Dense-AI/claude-scientific-skills 16,890
histolab Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
K-Dense-AI/claude-scientific-skills 16,890
deepchem 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.
K-Dense-AI/claude-scientific-skills 16,890
pdf Use this skill whenever the user wants to do anything with PDF files. This includes reading or extracting text/tables from PDFs, combining or merging multiple PDFs into one, splitting PDFs apart, rotating pages, adding watermarks, creating new PDFs, filling PDF forms, encrypting/decrypting PDFs, extracting images, and OCR on scanned PDFs to make them searchable. If the user mentions a .pdf file or asks to produce one, use this skill.
K-Dense-AI/claude-scientific-skills 16,890
datamol Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
K-Dense-AI/claude-scientific-skills 16,890
database-lookup Search 78 public scientific, biomedical, materials science, and economic databases via their REST APIs and return structured JSON results. Covers physics/astronomy (NASA, NIST, SDSS, SIMBAD, Exoplanet Archive), earth/environment (USGS, NOAA, EPA, OpenWeatherMap), chemistry/drugs (PubChem, ChEMBL, DrugBank, FDA, KEGG, DailyMed, ZINC, BindingDB), materials science (Materials Project, COD), biology/genomics (Reactome, BRENDA, UniProt, STRING, Ensembl, NCBI Gene, GEO, GTEx, PDB, AlphaFold, InterPro, ChEBI, BioGRID, Gene Ontology, QuickGO, NCBI Protein/Taxonomy, dbSNP, SRA, ENA, gnomAD, UCSC Genome, ENCODE, JASPAR, MouseMine, PRIDE, LINCS L1000, Human Protein Atlas, Human Cell Atlas, RummaGEO, Metabolomics Workbench, EMDB, Addgene), disease/clinical (COSMIC, Open Targets, ClinPGx, ClinicalTrials.gov, OMIM, ClinVar, GDC/TCGA, cBioPortal, DisGeNET, GWAS Catalog, Monarch, HPO), regulatory (FDA, USPTO, SEC EDGAR), economics/finance (FRED, BEA, BLS, Federal Reserve, World Bank, ECB, US Treasury, Alpha Vantage, Data Commons), and demographics (US Census, Eurostat, WHO). Use this skill whenever the user wants to look up compounds, drugs, proteins, genes, pathways, enzymes, gene expression, variants, clinical trials, patents, SEC filings, economic indicators, crystal structures, astronomical objects, earthquakes, weather, or any data from a public database API. Also trigger when the user mentions any database by name or asks about molecular properties, drug-target interactions, binding affinities, protein interactions, pathway membership, pharmacogenomics, economic time series, materials properties, commercially available compounds, virtual screening, compound purchasability, chemical libraries, building blocks, cancer genomics, somatic mutations, tumor mutation profiles, nucleotide sequences, genome assemblies, sequencing reads, ENA accessions, INSDC data, or wants to cross-reference entities across sources.
K-Dense-AI/claude-scientific-skills 16,890
dask Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
K-Dense-AI/claude-scientific-skills 16,890