115 件 ・ 関連度順
概要と使いどころ
A low-level plotting library for comprehensive customization. Use when fine-grained control over every plot element is needed, creating new types of charts, or integrating into specific scientific workflows. Can export to PNG/PDF/SVG for publication. For quick statistical charts, use seaborn; for interactive charts, use plotly; for journal-style, publication-ready multi-panel charts, use scientific-visualization.
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从数据剖析到出版级成图的完整可视化工具。先做数据剖析（列类型/样本量/分布/异常值/分组结构/相关性）， 再结合论证目标推荐图型，主动拦截科研画图经典错误，产出 Nature / Science / IEEE / Elsevier / PNAS / 中文核心期刊级别的成图。覆盖数据 EDA、图表契约、选图决策、出版级绘制、程序自检 + AI 读图闭环、多格式 导出和文件审计。数学建模场景额外支持三类图体系（原始数据/过程/结果）、子问题覆盖检查和建模流程图规范。 当用户涉及论文配图、科研画图、数据可视化、选图、期刊投稿图、figure、出版级图表、matplotlib、seaborn、 plotly、误差棒、显著性标注、色盲安全配色、矢量图导出、中文论文图表、多面板时使用。
日本語の概要は準備中です。原文の説明を表示しています。
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
日本語の概要は準備中です。原文の説明を表示しています。
Use this skill when visualising drone simulation results. Produces three matplotlib figures — desired vs actual trajectories, instantaneous error, and cumulative absolute error — for all 5 state groups (position, orientation, velocity, angular velocity, acceleration). Saves figures to a plots/ directory automatically.
日本語の概要は準備中です。原文の説明を表示しています。
数据可视化的判断层 SSOT——画任何图表、dashboard、看板之前先加载它，不是画完之后。管"该不该这么画"：这张图想让读者得出什么结论、用均值还是中位数、能不能聚合、堆叠还是折线、能不能上双轴、配色与颜色数量、同一实体跨图是否同色同位、表格该留几列。介质无关：HTML 报告页、React/Vue dashboard、PPT 原生图表、matplotlib/plotly/ECharts/D3、渲染成 PNG 的图，一律适用。Use when 动手画图之前，或评审既有图表（"这几张图有什么问题"、"图例为什么不一致"）。Not for 报告页的排版/组件/交互（用 report-with-html:report-with-html）或 PPT 原生图表的实现与渲染陷阱（用 deck-creator:deck-creator）。
日本語の概要は準備中です。原文の説明を表示しています。
Read and write Bluesky: view profiles and timelines, search posts, post, follow, read notifications. Trigger phrases: bluesky, atproto.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
Universal LaTeX document skill: create, compile, and convert any document to professional PDF with PNG previews. Supports resumes, reports, cover letters, invoices, academic papers, theses/dissertations, academic CVs, presentations (Beamer), scientific posters, formal letters, exams/quizzes, books, cheat sheets, reference cards, exam formula sheets, fillable PDF forms (hyperref form fields), conditional content (etoolbox toggles), mail merge from CSV/JSON (Jinja2 templates), version diffing (latexdiff), charts (pgfplots + matplotlib), tables (booktabs + CSV import), images (TikZ), Mermaid diagrams, AI-generated images, watermarks, landscape pages, bibliography/citations (BibTeX/biblatex), multi-language/CJK (auto XeLaTeX), algorithms/pseudocode, colored boxes (tcolorbox), SI units (siunitx), Pandoc format conversion (Markdown/DOCX/HTML ↔ LaTeX), and PDF-to-LaTeX conversion of handwritten or printed documents (math, business, legal, general). Compile script supports pdflatex, xelatex, lualatex with auto-detection, latexmk backend, texfot log filtering, PDF/A output, and verbosity control (--verbose/--quiet). Empirically optimized scaling: single agent 1-10 pages, split 11-20, batch-7 pipeline 21+. Use when user asks to: (1) create a resume/CV/cover letter, (2) write a LaTeX document, (3) create PDF with tables/charts/images, (4) compile a .tex file, (5) make a report/invoice/presentation, (6) anything involving LaTeX or pdflatex, (7) convert/OCR a PDF to LaTeX, (8) convert handwritten notes, (9) create charts/graphs/diagrams, (10) create slides, (11) write a thesis or dissertation, (12) create an academic CV, (13) create a poster, (14) create an exam/quiz, (15) create a book, (16) convert between document formats (Markdown, DOCX, HTML to/from LaTeX), (17) generate Mermaid diagrams for LaTeX, (18) create a formal business letter, (19) create a cheat sheet or reference card, (20) create an exam formula sheet or crib sheet, (21) condense lecture notes/PDFs into a cheat sheet, (22) create a fillable PDF form with text fields/checkboxes/dropdowns, (23) create a document with conditional content/toggles (show/hide sections), (24) generate batch/mail-merge documents from CSV/JSON data, (25) create a version diff PDF (latexdiff) highlighting changes between documents, (26) create a homework or assignment submission with problems and solutions, (27) create a lab report with data tables, graphs, and error analysis, (28) encrypt or password-protect a PDF, (29) merge multiple PDFs into one, (30) optimize/compress a PDF for web or email, (31) lint or check a LaTeX document for common issues, (32) count words in a LaTeX document, (33) analyze document statistics (figures, tables, citations), (34) fetch BibTeX from a DOI, (35) convert a Graphviz .dot file to PDF/PNG, (36) convert a PlantUML .puml file to PDF/PNG, (37) create a one-pager/fact sheet/executive summary, (38) create a datasheet or product specification sheet, (39) extract pages from a PDF (page ranges, odd/even), (40) check LaTeX package availability before compiling, (41) analyze citations and cross-reference with .bib files, (42) debug LaTeX compilation errors, (43) make a document accessible (PDF/A, tagged PDF), (44) create lecture notes or course handouts, (45) fill an existing PDF form (fillable fields or non-fillable with annotations), (46) extract text or tables from a PDF (pdfplumber, pypdf), (47) OCR a scanned PDF to text (pytesseract), (48) create a PDF programmatically with reportlab (Canvas, Platypus), (49) rotate or crop PDF pages (pypdf), (50) add a watermark to an existing PDF, (51) extract metadata from a PDF (title, author, subject).
日本語の概要は準備中です。原文の説明を表示しています。
读取Excel指定列的非空字符串数据，统计各元素数量，绘制饼状图，并在图中显示数量及括号内的占比，代码中不包含中文字符。
日本語の概要は準備中です。原文の説明を表示しています。
根据用户需求生成Python代码，用于读取CSV或Excel文件中的名称和数量列，绘制柱状图并保存为JPG格式。
日本語の概要は準備中です。原文の説明を表示しています。
Create publication-quality plots and visualizations using matplotlib and seaborn. Works with ANY LLM provider (GPT, Gemini, Claude, etc.).
日本語の概要は準備中です。原文の説明を表示しています。
Interactive visualization with Plotly. 40+ chart types (scatter, line, heatmap, 3D, geographic) with hover, zoom, pan. Two APIs: Plotly Express (DataFrame) and Graph Objects (fine control). For static publication figures use matplotlib; for statistical grammar use seaborn.
日本語の概要は準備中です。原文の説明を表示しています。
Graph and network analysis toolkit. Four graph types (directed, undirected, multi-edge), centrality, shortest paths, community detection, generators, I/O (GraphML, GML, edge list), matplotlib viz. For large graphs (100K+ nodes) use igraph or graph-tool; for GNNs use PyG.
日本語の概要は準備中です。原文の説明を表示しています。
Research posters in LaTeX using beamerposter, tikzposter, or baposter. Layout, typography, color schemes, figure integration, accessibility, and QA for conferences. Includes templates. For figure generation use matplotlib-scientific-plotting or plotly-interactive-plots.
日本語の概要は準備中です。原文の説明を表示しています。
3Dmol.js WebGL molecular visualization emitted as self-contained HTML. Render structures (PDB/SDF/XYZ/MOL2/cube) with stick, sphere, cartoon, line, and surface styles; animate trajectories with a frame-delay (interval, ms) control; and animate vibrational normal modes via vibrate() from per-atom dx/dy/dz displacements or from precomputed frames. Output standalone HTML that loads 3Dmol from a CDN, with optional play/pause and speed controls. Use for transition-state imaginary-mode animations, MD or reaction-path playback, docking poses, and orbital/density isosurfaces. For static 2D chemical structure drawings use rdkit-chemdraw-cdxml; for 2D statistical plots use matplotlib or plotly.
日本語の概要は準備中です。原文の説明を表示しています。
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
日本語の概要は準備中です。原文の説明を表示しています。
Low-level Python plotting for scientific figures: publication-quality line, scatter, bar, heatmap, contour, 3D; multi-panel layouts; fine control of every element. PNG/PDF/SVG export. Use seaborn for quick stats, plotly for interactive.
日本語の概要は準備中です。原文の説明を表示しています。
Interactive scientific visualization with Plotly. Two APIs: plotly.express (px) for one-liner DataFrame plots, plotly.graph_objects (go) for trace-level control. 40+ chart types with hover, zoom, pan, animation. Exports HTML or static PNG/SVG/PDF via kaleido. Use for volcano plots with gene hover, dose-response dashboards, expression heatmaps, 3D molecular views. Use seaborn for stats; matplotlib for publication figures.
日本語の概要は準備中です。原文の説明を表示しています。
omics-plotting: publication-style figure authoring for omics / bioinformatics results with matplotlib / seaborn. Read this before writing any plotting or figure code in any omics analysis — RNA-seq, proteomics, single-cell, variant, or database results — not only when a plot is explicitly requested: whenever an analysis will produce a figure, load this first and follow its recipes. Covers volcano, MA, expression / correlation heatmap, GSEA bar / dot plot, box / violin / bar / ridgeline, PCA / UMAP / t-SNE scatter, Kaplan–Meier, Manhattan / QQ / forest. Supplies a shared journal-ready style and copy-paste recipes so every figure looks like one consistent system. To combine several plots into ONE multi-panel composite figure, use the sibling `multipanel` skill.
日本語の概要は準備中です。原文の説明を表示しています。
Create visualizations for construction data. Generate charts, graphs, heatmaps, and interactive dashboards using Matplotlib, Seaborn, and Plotly for project analysis and reporting.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。
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.
日本語の概要は準備中です。原文の説明を表示しています。