4.2K skills · sorted by relevance
Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.
Check physical units, propagate measurement uncertainty through calculations, and format results with meaningful precision. Local tools also inspect Python code for unit and uncertainty mistakes and assess whether calculated magnitudes are physically plausible.
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.
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.
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.
Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.
Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.
Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.
Prepare papers, research posters, and grant documents for a specific journal, conference, or funder. The skill guides official template selection, checks current submission requirements, adapts writing, and inspects PDF page counts and embedded fonts.
Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.
Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.
Use SymPy to simplify expressions, solve equations, and calculate derivatives, integrals, and matrices with exact symbolic results. Specify assumptions, verify solutions, and turn mathematical expressions into numerical functions, source code, or LaTeX output.
Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.
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.
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.
Assess scientific claims by examining study design, statistics, bias, and evidence quality. Use structured appraisal frameworks to identify supported conclusions, explain uncertainties, and propose practical improvements with concerns traced to specific evidence.
Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.
Turn descriptions of scientific components and relationships into PNG diagram drafts with Nano Banana 2. Gemini 3.7 Flash reviews each draft and may request one revision, while saved image versions and review logs support manual checking.
Use PyTDC to discover therapeutic machine-learning datasets, create task-appropriate training and test splits, and evaluate predictions against curated benchmarks. Also score selected molecules with supported scoring functions, with explicit download plans and bounded outputs.
Explore recommended papers in Paperzilla projects, search project feeds, and summarize available paper text. The skill also helps explain research relevance, record recommendation feedback, export records as JSON, and retrieve personal feed URLs.
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.
Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.
Research current web information with Parallel CLI and organize findings with supporting sources. The skill covers search, page extraction, detailed reports, adding sourced fields to existing lists, finding matching entities, and recurring monitoring of web changes.
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.