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Create, polish, and troubleshoot figures4papers-style publication-ready matplotlib figures, including grouped bars, trends, radar charts, heatmaps, concept diagrams, manifolds, and high-DPI/vector export workflows.
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Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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Create clear, effective charts and dashboards from structured data using matplotlib, seaborn, and plotly. Use when the user requests data visualization or provides relevant inputs for this workflow.
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Use this when creating, modifying, reviewing, or saving charts, figures, plots, or visual summaries — including matplotlib/seaborn code, EDA figures, report figures, dashboards, or any task involving Japanese chart labels, color palettes, or figure styling.
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Compose polished charts (timeseries, bar, line, area, pie, scatter, or anything else the data calls for) from tabular data using the chartkit primitives, producing PNG, SVG, or self-contained interactive HTML. Use when the user asks to chart, graph, plot, or visualize data and wants something better than raw matplotlib defaults.
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Use when 用户请求选择、创建、改进或统一数据可视化，并需按数据与意图选择图表、主题及 D3.js、ECharts、Mapbox、Three.js、Matplotlib、Plotly 或 ggplot2 技术栈。
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Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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Create standardized charts and visual assets from analytics query results. Prefer dependency-free plain HTML/CSS/JavaScript/SVG for interactive exploratory charts; use local plotting tools such as matplotlib, seaborn, or Plotly when static/report-ready exports or specialized charting libraries are more appropriate. Use for trends, comparisons, distributions, report assets, CSV-to-chart work, and presentation-ready data visuals.
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Use when analyzing pcap or pcapng files, triaging network captures, labeling IPs with evidence, generating PNG charts, or writing packet analysis reports. Keywords: pcap, pcapng, tshark, Wireshark, scapy, DNS, TLS SNI, RDAP, graph, matplotlib, gnuplot, packet capture.
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Chọn đúng biểu đồ, kể chuyện với số liệu, và tránh误导 — dựa trên nguyên tắc từ Kieran Healy (Data Visualization, Princeton 2019) và taxonomy từ AntV chart-visualization-skills. Dùng khi cần quyết định loại chart, trình bày insight cho stakeholder, hoặc kiểm tra xem figure có gây hiểu lầm không. Để vẽ Python thực tế, chuyển sang matplotlib / seaborn / scientific-visualization.
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Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
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Use when designing, redrawing, or validating research-paper figures and tables, including Matplotlib plots, LaTeX tables, benchmarks, captions, legends, annotations, color, and typography. Preserves exact evidence and checks rendered PDFs for clipping, overlap, and readability.
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Use when maintaining sports field visualization or Tkinter GUI and standalone CLI integration with Matplotlib.
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Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
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Compose multi-panel publication figures with patchwork, cowplot, gridExtra (R), or matplotlib GridSpec/subfigures (Python) including shared axes/legends/guides collection, panel labels in Nature/Cell convention, and journal-spec sizing. Covers patchwork ≥1.2.0 axes='collect' feature, Type-42 font embedding, and the cairo_pdf save path. Use when composing 2+ subpanels into a single figure for journal submission.
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学术论文图表生成与设计。输入是实验数据（CSV/表格）、图表草稿、论文方法描述、中文图/表说明、想生成的图形摘要（GA）、或想画流程图时使用。功能包括：matplotlib 绘图（plot）、图表类型推荐（recommend）、英文图/表标题生成（caption）、论文架构图 prompt 生成（architecture）、图形摘要 prompt 生成（abstract）、流程图 prompt 生成（flowchart）、图表规范检查（polish）、概念图/示意图设计（design）。架构图模板覆盖 6 类场景，流程图覆盖 6 类（线性 / 分支 / 并行 / 循环 / 嵌套 / 泳道），图形摘要覆盖 5 类。内置 prompt 工程词汇表与参数化语法。不适用于：正文段落润色（用 academic-writing）、参考文献格式（用 academic-citation）。
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Submission-grade remote-sensing Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, map-led composites, remote-sensing image plates, sensor/product workflow figures, validation/error/uncertainty panels, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family, Remote Sensing of Environment, ISPRS, IEEE TGRS, or other high-impact venues. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics. Also trigger on general academic-writing figure needs even without the word "Nature", such as making figures/plots for a paper, scientific/academic plotting, data visualization for a manuscript, and Chinese phrasings like 遥感论文配图、学术写作配图、科研绘图、科研作图、地图出图、论文图表、可视化.
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Guidelines for data analysis and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
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Expert guidance for data analysis, visualization, and Jupyter Notebook development with pandas, matplotlib, seaborn, and numpy.
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Data analysis best practices with pandas, numpy, matplotlib, seaborn, and Jupyter notebooks.
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Publication-quality physics plots — vector fields, streamlines, contour maps, 3D surfaces, phase space, spectrograms, and animations. Optimized for journal submission with LaTeX labels, proper colormaps, and multi-panel layouts.
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Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial analysis, geometric operations, coordinate transformations, spatial joins, overlay operations, choropleth mapping, or any task involving reading/writing/analyzing vector geographic data. Supports PostGIS databases, interactive maps, and integration with matplotlib/folium/cartopy. Use for tasks like buffer analysis, spatial joins between datasets, dissolving boundaries, clipping data, calculating areas/distances, reprojecting coordinate systems, creating maps, or converting between spatial file formats.
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Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.
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Build volcano and MA plots from differential-expression / association results with LFC shrinkage, FDR-adjusted thresholds, sensible label placement, and axis-truncation conventions. Covers EnhancedVolcano, ggplot2, matplotlib, and the apeglm/ashr/normal shrinkage decision. Use when visualizing differential-expression results (RNA-seq, ChIP-seq, ATAC-seq, proteomics) or any per-feature effect-size + p-value table.
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