SciPy plugins

Run 80+ bioinformatics workflows locally — pharmacogenomics, GWAS, single-cell RNA-seq, ancestry, metagenomics, variant annotation, protein structure prediction, and clinical reporting — with deterministic Python execution, reproducibility bundles, and privacy-preserving local computation.

Guides Chinese academic paper writing from brainstorming through publication: structures research plans, generates literature reviews with BibTeX citations, produces publication-quality Python charts (matplotlib/seaborn), and outputs LaTeX-formatted manuscripts with journal templates.

Provides 197 computational skills for scientific AI agents to perform life sciences research, covering genomics, proteomics, drug discovery, medical imaging, biostatistics, and scientific writing via integrations with databases, analysis tools, and ML frameworks.

Conduct scientific research across 1000+ biomedical databases (PubMed, UniProt, PubChem, TCGA, FAERS, ClinicalTrials.gov, etc.) through an MCP server, research agent, slash commands, and 115 specialized skills for drug discovery, genomics, clinical trials, and literature review.

Design provably correct, asymptotically optimal algorithms and data structures; analyze computational complexity with Big-O proofs; implement dynamic programming, graph algorithms, sorting/searching, numerical methods, and optimization solvers; optimize performance-critical code across paradigms like greedy, divide-and-conquer, and backtracking.

Execute astrophysics analysis workflows: compute JAX-accelerated bandflux for supernova light curves with synthetic data and likelihood templates; process FITS files via photometry, spectroscopy, astrometry, light curve analysis, and cosmological calculations using astropy ecosystem.

Streamline end-to-end data science and ML workflows: frame business problems into ML tasks, preprocess and validate data with quality checks, perform EDA on diverse formats, design and execute experiments with hyperparameter tuning via Optuna and interpretability via SHAP, audit reproducibility and leakage, evaluate model performance and readiness for deployment, generate model cards, and extract structured learnings into docs.