Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
WHAT YOU BECOME
Perfect for these scenarios
Quickly package and analyze code for vulnerabilities without manual file hunting.
Generate structured snapshots for LLMs to create accurate documentation.
Evaluate external codebases before integration with full context preservation.
Create time-stamped, AI-friendly code archives for version control or analysis.
MEASURED GAIN
Proven benefits and measurable impact
Reduce hours of manual file gathering into minutes with automated packaging.
Minimize token usage while preserving full context for efficient AI processing.
Ensure comprehensive code context for more reliable AI-driven insights.
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 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.
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.
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