Matplotlib 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.

Automates the full academic research workflow: literature search, data processing, idea validation, experimental design, paper drafting and polishing, publication-ready figure generation, peer review simulation, rebuttal crafting, compliance auditing, patent/software registration, and presentation creation, with integrity checks and multi-format typesetting.

Generate academic research artifacts including hypotheses, experimental protocols, paper drafts, data visualizations, and peer review simulations from notes and documents.

Generate publication-quality Matplotlib and Seaborn charts and diagrams featuring colorblind-accessible palettes, despined axes, and rich annotations for professional data visualizations, plots, and diagrams in Python workflows.

Scaffold LaTeX projects for NeurIPS/CVPR/IEEE EECS papers, draft/revise sections via TEEL framework with word budgets, generate matplotlib/seaborn figures, manage bibtex citations and compliance, simulate peer reviews with 5 personas and stress tests, process feedback into revision roadmaps, compile with pdflatex/bibtex pipelines and error fixes, remove AI writing patterns.

Streamline ML conference paper writing: draft abstracts, introductions, related work, experiments, discussions; generate posters, slides, animated GIFs; verify references; synthesize reviews and rebuttals.

Automate publication-quality plots (matplotlib/seaborn), TikZ diagrams, and figures for academic papers: render outputs, audit for visual defects like overlaps/collisions/truncation, auto-fix issues, upgrade styles with colorblind palettes and embedded fonts, apply venue templates (NeurIPS/ICML/ICLR/etc.), iterating via render-view-fix loop until defect-free.

Search arXiv, INSPIRE-HEP, NASA ADS, HEPData, and Zenodo for physics papers and experimental data by keywords, authors, or IDs; download BibTeX, LaTeX source, CSV/YAML/JSON/ROOT files; visualize posterior samples from PolyChord/MultiNest with corner plots, marginals, and KL divergence using matplotlib/pandas.

Enforce consistent LaTeX formatting and generate publication-quality figures and diagrams for academic papers, especially machine learning conference submissions, using automated checks and TikZ/PGF vector graphics.

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.

Run a multi-agent research analysis pipeline on ML papers: parse LaTeX/PDF/arXiv into structured data, then fan out to specialized agents for evidence verification, proof checking, statistical validity, notation consistency, figure design, and rebuttal generation, with parallel execution for 2-3x speedup.

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.

Drive autonomous iteration loops for research tasks, generate publication-quality data visualizations and scientific code, fetch arXiv full-text and BibTeX citations, create images via Gemini, and stream text-to-speech.

Generate publication-quality academic diagrams and statistical plots from descriptions, LaTeX, or Markdown files using a Gemini-powered multi-agent pipeline. It retrieves reference examples, plans detailed specs, renders via Matplotlib or Gemini API, critiques outputs, and refines aesthetics for NeurIPS standards, enabling iterative high-fidelity research visuals.

Automates end-to-end content creation: collect hot topics from RSS feeds, generate platform-optimized articles for WeChat, Xiaohongshu, and Feishu with AI drafting, enforce compliance review, and publish via automated browser and API workflows. Also supports analyzing content performance data with statistical charts and reports.