'figures' 검색 결과

Create patent line drawings from text or reference images through the PatentFig AI API. Convert existing drawings into scalable vector files for CAD, increase image resolution, and export figures in formats used for patent filing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating patent figures from invention descriptions
• Generating drawings based on sketches or photos
• Converting existing drawings into SVG or DXF

Create paper figures and LaTeX comparison tables from experiment data in JSON or CSV files. Save reproducible plotting scripts, use consistent styling, and check rendered figures for readability, fair comparisons, and captions supported by the data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plotting training curves
• Comparing methods and ablation results
• Combining results across datasets into multiple panels

Create consistent paper figures and comparison tables from experiment data. The skill selects suitable chart types, saves reusable plotting scripts, prepares LaTeX insertion code, and checks rendered figures for readability and comparisons for consistency with the underlying data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plot training curves from experiment results
• Create method comparison and ablation tables
• Combine results across datasets into multi-panel figures

Prepare descriptions for supplied patent drawings by identifying components, assigning reference numerals, and mapping them to claim elements. Produce a numeral index and flag missing figures or labels while leaving the original drawings unchanged.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting descriptions for patent drawings
• Organizing component reference numerals across figures
• Checking drawing coverage for claim elements

Prepare scientific figures for journal submission with consistent panel layouts, error bars showing uncertainty, statistical significance labels, and accessible colors. Use matplotlib, seaborn, or plotly to adjust figure dimensions and export publication formats.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing figures for journal submission
• Combining multiple plots with consistent styling
• Making figures readable in color and grayscale

Icarus Figures produces publication-grade figures for scientific papers — reproducible matplotlib/seaborn data figures and original TikZ method/framework/architecture figures, held to a four-axis quality bar (depth · elegance · unimpeachable · visible-gap) and exported as editable PDF+PNG+SVG with a runnable critique gate. Use this whenever a figure is destined for a paper, journal, conference submission, thesis, or report — making a plot/chart publication- or journal-ready, building a method/architecture/framework diagram or 技术路线图 (the hero figure a paper opens with), a schematic or workflow figure, or cleaning up a matplotlib/seaborn/TikZ figure that currently looks like a homework plot. Trigger even when the user never says "publication": if the figure goes into a written scientific document, this skill applies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn structured JSON into editable SVG diagrams for system architectures, workflows, and pipelines. Specify positions, connections, and groups precisely, reproduce the same output under fixed rendering conditions, and convert figures to PDF for papers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating architecture diagrams for technical papers
• Illustrating workflows and processing pipelines
• Adjusting diagram layouts through JSON specifications

Creates figures for machine learning papers from draft text or experiment data. Extracts components for Gemini-generated diagrams, chooses suitable charts with matplotlib or seaborn, and adjusts colors, fonts, and export formats for publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a method section into an architecture diagram
• Create comparison charts from experiment results
• Plot training loss and accuracy over time

Prepare scientific figures for papers, reports, and saved deliverables by checking data fidelity, chart choice, labels, axes, and color consistency. Verify exported figures for overlapping text and readability while keeping fonts, palettes, and framing configurable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing scientific figures for a paper or report
• Checking excluded data and summary statistics
• Improving labels, axes, and color consistency

Create animated lesson diagrams, sliders that update figures, and interactive panels that explain how systems work. It also builds moving dot patterns for page headers, with rules for readability, themes, and reduced motion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Animate a process in a lesson
• Explore a formula with a slider
• Show a system's behavior through one control

Create and review scientific figures with Matplotlib, Seaborn, or Plotly. The skill covers panel layouts, uncertainty, missing data, accessible color choices, and exported file checks, helping prepare figures for papers, presentations, or web publication.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create multi-panel figures for a paper
• Show uncertainty and missing observations clearly
• Review palette contrast and color-independent cues

Create figures for machine learning papers from text or experiment data. Uses Gemini for architecture diagrams and matplotlib/seaborn for numerical charts, choosing suitable layouts and chart types while preparing consistent styling, export files, and reusable generation scripts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning a methods section into an architecture diagram
• Charting model comparisons and ablation results
• Plotting training curves and confusion matrices

Style every matplotlib figure like the Stata 18/19 default (stcolor) scheme — Arial embedded as TrueType, white background, recessive light-gray grid below the data, no top/right spines, unframed legends, and the validated blue/red/gray palette. Use whenever a task generates or restyles charts, plots, or figures for papers, reports, or slides, even if the user doesn't mention Stata — this is the house style for all publication figures. Also use when asked to make figures "look like Stata", match the stcolor scheme, or restyle existing matplotlib output.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create static SVG diagrams, scalable images for lessons, READMEs, and manuals. It uses real system names, nine diagram types, and consistent colors and arrows, with checks for readable labels, accessible descriptions, and valid markup.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add a diagram of system interactions to a README
• Explain state transitions in a manual
• Illustrate data flow from logs or specifications

Create Chinese-English reading materials that connect a scientific paper’s text with its figures, tables, and equations. It also handles selected excerpts and source-linked questions, keeping translations and explanations tied to the available original material.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a Chinese-English reader for a full paper
• Translate a selected passage from a paper
• Read paper text alongside figures, tables, and equations

Update figures throughout an existing presentation while preserving its formatting. Find equivalent values across text, tables, and chart data, present a change list for approval, and flag related growth rates or percentages that may need recalculation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Updating a presentation with quarterly results
• Refreshing figures in peer comparison slides
• Replacing values using an Excel update sheet

Choose suitable charts for trends, comparisons, distributions, and relationships, then create them in Python with matplotlib, seaborn, or plotly. Apply guidance on colors, labels, scales, and accessibility to produce publication-quality figures or interactive charts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a chart type that fits the data and message
• Create figures for reports or publications
• Build interactive charts with Plotly

Create magazine-style pages for long blog posts, essays, and case studies. The layout combines readable single-column text with a prominent title, captioned figures, highlighted quotations, author details, and related article cards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing a long-form blog page
• Presenting an essay with magazine-style typography
• Laying out a case study with figures and quotations

Draft and revise machine learning, computer vision, and natural language processing papers. Improve section structure, paragraph flow, and figures or tables, then check major claims against experimental evidence and review the draft from a skeptical reviewer’s perspective.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting or revising an abstract and introduction
• Improving paragraph flow and terminology consistency
• Checking whether experiments support major claims

Create SVG illustrations of active figures using one continuous black line and offset color patches that resemble misaligned printing. The skill guides poses, colors, composition, rendering, and visual checks for product pages and announcement cards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Illustrations for empty states and 404 pages
• Playful figures for onboarding screens
• Artwork for blog headers and launch announcements

Write mathematical modeling competition papers from existing analysis reports, results, and figure PDFs. Use competition templates in Typst or LaTeX, tools for document layout, to organize sections, place figures, and prepare references and summaries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn modeling reports into a competition paper
• Use a template for MCM or CUMCM
• Place existing figures in relevant sections

Draft or restructure scientific manuscripts using Nature-style argument patterns, starting from supplied results, figures, notes, or Chinese drafts. It organizes sections, connects claims to evidence, and identifies missing inputs while keeping conclusions within the research's demonstrated limits.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting an abstract from research results and figures
• Rebuilding an introduction around the research gap
• Turning Chinese research notes into English manuscript prose

Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Debugging MATLAB signal-processing scripts
• Analyzing sensor time series in Python
• Checking units and assumptions in optical simulations

Create charts in Python with detailed control over colors, axes, labels, legends, and layouts. Use it to build scientific figures, arrange multiple plots, and export results as PNG, PDF, or SVG files for papers and presentations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating figures for papers and presentations
• Arranging multiple charts in one figure
• Adjusting colors, axes, labels, and legends