NumPy 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.

Automate end-to-end ML performance investigations: research SOTA papers and architectures, generate phased plans, judge experimental methodologies, profile bottlenecks, run metric-improvement campaigns with atomic git commits, auto-rollback on regressions, and leverage specialist agents for data lifecycle and deep paper analysis.

Bootstrap Claude Code with 17 specialized agents, skills, and hooks to audit/evolve .claude/ configs, engineer/refactor Python code via TDD, profile/optimize ML workloads, generate docs/tests, design systems, diagnose issues, and manage workflows professionally.

Generate optimized SQL queries from natural language for BigQuery, PostgreSQL, MySQL, and Snowflake; perform cohort analysis on CSV/Excel user data to compute retention rates, visualize trends, and detect anomalies; evaluate A/B tests with statistical significance, confidence intervals, and launch recommendations.

Automate academic research in finance, economics, and real estate: search literature via Corbis, generate and rank novel ideas with heuristics, screen for novelty and journal fit, visualize trends and gaps with Python-generated figures, draft structured reviews and positioning memos, read/summarize papers with a subagent, and audit citations in LaTeX files.

Perform high-performance steady-state analysis on distribution power systems—including power flow, state estimation, and IEC 60909 short-circuit calculations—using Python's power-grid-model library with numpy structured arrays for batch and parallel simulations across 22 component types.

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.

Turn any topic into a compact expertise artifact through a structured research pipeline (question tree, source discovery, zero-context fetch, .mv2 indexing, REPL distillation) without loading raw content into LLM context. Execute Python code in isolated Docker sandboxes for data analysis, prototyping, and DSPy sub-agents. Query Neo4j graph databases for knowledge retrieval.