Matplotlib plugins

Automate Chinese research paper workflows: brainstorm topics and structures interactively, synthesize literature from PubMed/arXiv with BibTeX, generate Python data visualizations/stats code using matplotlib/seaborn/scipy, draft evidence-driven chapters, peer-review with checklists, polish bilingual text, and compile publication-ready LaTeX with journal templates.

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.

Automate end-to-end chip and mask layout design in KLayout using AI agents via MCP server: detect van der Waals flakes from microscope images, align GDS to photos, trace contours to polygons, route nanodevice contacts to pads, overlay images, toggle layers, capture visuals, and export GDS from natural language queries.

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.

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.

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.

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.

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.

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.

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.

Automate end-to-end content creation: collect RSS hotspots and trends, generate viral AI-drafted articles for WeChat Public Account, Xiaohongshu, and Feishu with style imitation and visuals, review for quality/compliance risks, analyze performance from data files with charts, and publish via APIs or Playwright browser automation after one-time credential setup.