Data & Analytics

--> --- name: bio-spatial-statistics description: "Comprehensive spatial statistics toolkit \u2014 cluster-level (neighborhood\ \ enrichment, Ripley, co-occurrence), gene-level (Moran's I, Geary's C, local Moran,\ \ Getis-Ord), and network-level analysis." tool_type: mixed primary_tool: spatial measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **Spatial Statistics**, the spatial autocorrelati...

--> --- name: bio-spatial-preprocess description: Load spatial transcriptomics data (Visium, Xenium, MERFISH, Slide-seq, generic h5ad), perform QC filtering, normalization, HVG selection, PCA, UMAP, and Leiden clustering. tool_type: mixed primary_tool: spatial measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **Spatial Preprocess**, the foundation skill of OmicsClaw spatial analysis. Your rol...

--> --- name: bio-spatial-genes description: Find genes with spatially variable expression patterns using Moran's I, SpatialDE, SPARK-X, or FlashS. Identifies genes whose expression is non-randomly distributed across tissue coordinates. tool_type: mixed primary_tool: spatial measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **Spatial Genes**, the spatially variable gene (SVG) discovery skill ...

--> --- name: bio-spatial-domains description: Identify tissue regions and spatial niches from preprocessed spatial transcriptomics data using Leiden, Louvain, SpaGCN, STAGATE, GraphST, or BANKSY. tool_type: mixed primary_tool: spatial measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **Spatial Domains**, a specialised OmicsClaw agent for tissue region and spatial niche identification. Your r...

--> --- name: bio-spatial-de description: "Differential expression analysis \u2014 find marker genes for clusters\ \ or compare two groups. Supports Wilcoxon rank-sum, t-test, and PyDESeq2 methods\ \ with publication-ready figures and CSV tables." tool_type: mixed primary_tool: spatial measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command --- You are **Spatial DE**, the differential expression and marker gen...

R is a premier language for statistical computing and graphics, serving as the backbone for much of modern bioinformatics, particularly through the Bioconductor project. Mastery of R is essential for genomic data analysis, rigorous statistical testing, and publication-quality visualization.

每次科研任务开始时，生成一个临时的本地 dashboard，动态展示任务关键信息和产物预览。

--> --- name: bio-paper-reproduce description: 'Systematic methodology for reproducing published academic papers using provided data. Use when the user asks to reproduce, replicate, or verify results from a published paper, including sample selection, descriptive statistics, regression analyses, and generating reproduction reports (Markdown + LaTeX PDF). Covers the full pipeline: data exploration, variable identification/mapping, sample filtering, variable construction, statistical analysis, ...

--> --- name: bio-xlsx description: "Use this skill any time a spreadsheet file is the primary input or output.\ \ This means any task where the user wants to: open, read, edit, or fix an existing\ \ .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting,\ \ charting, cleaning messy data); create a new spreadsheet from scratch or from\ \ other data sources; or convert between tabular file formats. Trigger especially\ \ when the user references a spreadsheet fil...

--> --- name: bio-vaex description: Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not f...

--> --- name: bio-usfiscaldata description: 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...

--> --- name: bio-umap-learn description: UMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-torch-geometric description: Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-timesfm-forecasting description: Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with ...

--> --- name: bio-statsmodels description: Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 min...

--> --- name: bio-shap description: Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch),...

--> --- name: bio-seaborn description: Statistical visualization with pandas integration. Use for quick exploration of distributions, relationships, and categorical comparisons with attractive defaults. Best for box plots, violin plots, pair plots, heatmaps. Built on matplotlib. For interactive plots use plotly; for publication styling use scientific-visualization. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minute...

--> --- name: bio-scvelo description: RNA velocity analysis with scVelo. Estimate cell state transitions from unspliced/spliced mRNA dynamics, infer trajectory directions, compute latent time, and identify driver genes in single-cell RNA-seq data. Complements Scanpy/scVI-tools for trajectory inference. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-scikit-learn description: 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. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skil...

--> --- name: bio-scikit-bio description: Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-scientific-visualization description: Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow su...

--> --- name: bio-pytdc description: Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---

--> --- name: bio-pyopenms description: Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output withi...

--> --- name: bio-pymc description: Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference. tool_type: mixed primary_tool: Unknown measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command ---