K-Dense

Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.

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• Reading selected regions of large scientific arrays
• Choosing data blocks and compression settings
• Working with scientific arrays stored on S3 or GCS

Retrieve U.S. federal financial data from the Treasury’s public API, including debt, revenue, spending, interest rates, and exchange rates. Filter dates and fields, retrieve multiple pages, and organize results into tables for analysis.

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• Tracking U.S. national debt over time
• Comparing monthly federal revenue and spending
• Retrieving Treasury reporting exchange rates

Build and test SimPy models of queues, production, logistics, and other event-driven systems. Set execution limits, monitor waiting times and resource use, and analyze repeated runs with recorded random seeds for reproducible results.

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• Comparing waiting times with different staffing levels
• Modeling shared equipment in production or logistics
• Testing priority queues and interrupted work

Simulate lithium-ion battery charging, discharging, and rest with PyBaMM. Track voltage and capacity, record parameter sources, check how numerical settings affect predictions, and compare simulated curves with measured cycling data.

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• Predicting voltage changes during charge, discharge, and rest
• Comparing simulation results with measured cycling data
• Checking how numerical settings affect predictions

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

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• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

Find biological pathways and functions enriched in gene lists or ranked gene data. Choose an appropriate statistical method, align gene identifiers, define comparison backgrounds, and turn results into interpretable tables and plots with multiple-testing corrections.

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• Finding enriched pathways among differentially expressed genes
• Running GSEA on a complete ranked gene list
• Calculating gene-set scores for individual samples or cells

Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.

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• Inspect, convert, and preprocess mass spectrometry files
• Build quantitative tables across multiple samples
• Find metabolite candidates from accurate mass

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Combine laboratory animal welfare measurements into reference-relative RELSA severity scores. Analyze individual trajectories, explore forecasts for specified observation times, and derive candidate score zones, with explicit checks on normalization, reference selection, prediction intervals, and evaluation limits.

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• Combine animal welfare measurements into daily scores
• Compare group severity using a shared reference
• Explore severity forecasts at specified observation times

Analyze biological sequences and microbial communities in Python. Process and align DNA, RNA, or proteins, build evolutionary trees, calculate diversity, visualize sample differences, test group differences, and convert common biological file formats.

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• Calculate and compare microbiome diversity
• Align DNA, RNA, or protein sequences
• Build and compare evolutionary trees

Create research presentation slides with clear structure, concise text, figures, and timing guidance. The skill supports PowerPoint, LaTeX Beamer, and image-based PDFs, with workflows for preserving original research charts and reviewing slide layouts.

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• Prepare slides for a conference research talk
• Build a thesis or dissertation defense presentation
• Adapt paper content for a seminar presentation

Organize PyTorch models and data pipelines with PyTorch Lightning, automate training, save checkpoints, and track experiments. Includes templates and guidance for configuring training across multiple GPUs or TPUs.

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• Restructure PyTorch training code into LightningModules
• Build reusable data preparation and loading pipelines
• Configure checkpoints, early stopping, and experiment logs

Build quantum circuits with Qiskit, test them locally, and prepare them for IBM quantum hardware. The skill covers hardware-specific circuit conversion, sampling, expectation values, and analysis of measurement results, uncertainty, and resource usage.

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• Building and simulating quantum circuits locally
• Adapting circuits to IBM quantum hardware
• Comparing sampled results and expectation values

Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.

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• Checking associations between genes, drugs, and diseases
• Summarizing genes, drugs, and phenotypes linked to a disease
• Exploring short paths between two entities

Draft and revise scientific manuscripts while linking claims to verified evidence. The skill organizes source records, checks consistency across text and results, tracks reporting requirements, and reviews references and disclosures, with human verification and final approval.

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• Drafting manuscript sections from verified evidence
• Checking consistency across text, tables, and results
• Reviewing references and reporting checklist coverage

Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.

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• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

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• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Build and check clinical machine learning workflows with PyHealth, from loading electronic health records, signals, or images to training and evaluation. Includes patient-level data splits, prediction task checks, model checkpoints, and medical code mapping.

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• Prototype mortality and readmission prediction models
• Check patient splits and data leakage
• Train and evaluate sleep staging models

Gather scholarly evidence for a scientific manuscript or research brief. Organize supporting and conflicting findings into references, study comparison tables, and claim-to-source maps, with search records and coverage reports that help identify gaps requiring further review.

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• Gather background literature for a manuscript
• Compare supporting, conflicting, and null findings
• Build study comparison tables and claim-to-source maps

Build and optimize quantum circuits with PennyLane, including models that combine quantum circuits with PyTorch or JAX. Explore molecular energy estimation and optimization problems, validate values and gradients in simulation, and prepare tested circuits for provider plugins.

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• Check quantum circuit values and gradients in simulation
• Train hybrid quantum and PyTorch or JAX models
• Estimate molecular energies with VQE

Review papers, drafts, and research ideas with evidence-linked feedback focused on improvement. Audit low-stakes assessment rubrics, trace supporting evidence, and check rater agreement and uncertainty without ranking people or recommending consequential decisions.

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• Prepare evidence-linked feedback on a paper draft
• Review strengths and limitations of a research idea
• Check assessment criteria and evidence traceability

Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.

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• Predicting properties across a compound library
• Exploring molecular shapes and tautomeric forms
• Estimating how compounds bind to proteins

Analyze single-cell RNA-seq data with Scanpy, from quality checks and normalization to cell grouping, exploratory marker genes, and plots. Bundled scripts also prepare sample-level count summaries and support conversion of R objects to h5ad.

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• Check data quality and normalize single-cell measurements
• Visualize cell groups with UMAP or t-SNE
• Explore marker genes and review cell-type labels

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

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• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps