Use this skill when working with symbolic mathematics in Python. This skill should be used for symbolic computation tasks including solving equations algebraically, performing calculus operations (derivatives, integrals, limits), manipulating algebraic expressions, working with matrices symbolically, physics calculations, number theory problems, geometry computations, and generating executable code from mathematical expressions. Apply this skill when the user needs exact symbolic results rather than numerical approximations, or when working with mathematical formulas that contain variables and parameters.
WHAT YOU BECOME
Perfect for these scenarios
Retrieve gene info from Ensembl/UniProt for research or validation.
Perform quick BLAST queries against multiple databases for sequence analysis.
Use Enrichr to identify enriched pathways in gene expression data.
Download genome data or batch process queries efficiently via CLI.
MEASURED GAIN
Proven benefits and measurable impact
Reduce query time with direct CLI access to 20+ databases.
Skip custom scripts; use built-in commands for common bioinformatics tasks.
Accelerate research workflows with seamless multi-database integration.
WHAT YOU GET
Files, tags and the three-step install
Tip: Read the documentation and the code before first use, so you know what it does and which permissions it needs.
NEXT SCRIPTS
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This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
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