Back to Skills

render-publication-graphic

pjt222
Updated Yesterday
5 views
17
2
17
View on GitHub
Designgeneral

About

This skill generates publication-ready 2D graphics with correct DPI, color profiles, and typography for both print and digital use. It's designed for preparing journal figures, print materials, and meeting technical specifications, supporting multi-format exports from a single source. Developers can use it to automate the creation of graphics that comply with strict publication requirements.

Quick Install

Claude Code

Recommended
Primary
npx skills add pjt222/agent-almanac -a claude-code
Plugin CommandAlternative
/plugin add https://github.com/pjt222/agent-almanac
Git CloneAlternative
git clone https://github.com/pjt222/agent-almanac.git ~/.claude/skills/render-publication-graphic

Copy and paste this command in Claude Code to install this skill

Documentation

Render Publication Graphic

Produce pub-ready graphics meeting tech req for journals, books, presentations, web. DPI, color space, typography, file format select, metadata embed.

Use When

  • Prep figures for journal submission
  • Graphics for print pubs (books, mags)
  • High-quality assets for presentations
  • Web pubs w/ proper opt
  • Meet pub tech specs
  • Archive w/ proper metadata
  • Multi-format from single source

In

InputTypeDescriptionExample
Source graphicFile/DataOriginal visualization or artworkSVG, R ggplot, Python matplotlib, Blender render
Publication targetSpecificationJournal, web, print, presentationNature journal, IEEE paper, website
Technical requirementsParametersDPI, dimensions, color space, format300 DPI, 180mm width, CMYK, TIFF
Style guideDocumentPublisher typography and formatting rulesFont families, line widths, color palette
MetadataInformationTitle, author, date, copyright, descriptionFigure caption, license info

Do

1. Determine Output Req

ID tech specs for target pub:

# Common publication requirements

academic_journal:
  dpi: 300-600
  format: TIFF, EPS, PDF
  color_space: RGB or CMYK (check guidelines)
  max_width: 180mm (single column) or 390mm (double column)
  fonts: Embed or outline
  resolution_minimums:
    line_art: 1000 DPI
    halftone: 300 DPI
    combination: 600 DPI

web_publication:
  dpi: 72-96 (retina: 144-192)
  format: PNG, WebP, SVG
  color_space: sRGB
  max_file_size: 200KB-500KB
  optimization: Compress, progressive loading

presentation:
  dpi: 96-150
  format: PNG, PDF, SVG
  color_space: RGB
  dimensions: 16:9 or 4:3 aspect ratio
  contrast: High contrast for projectors

print_book:
  dpi: 300-600
  format: TIFF, PDF/X
  color_space: CMYK
  bleed: 3-5mm beyond trim
  fonts: Embedded

→ Clear understanding of target req If err: contact pub for specific guidelines, use conservative defaults

2. Set Correct DPI for Raster

Configure resolution by output medium:

from PIL import Image

def set_dpi_pillow(image_path, output_path, target_dpi=300):
    """Set DPI metadata for PNG/TIFF."""
    img = Image.open(image_path)

    # Save with DPI metadata
    img.save(output_path, dpi=(target_dpi, target_dpi))
    print(f"Saved with {target_dpi} DPI: {output_path}")

def calculate_dimensions(width_mm, height_mm, dpi=300):
    """Calculate pixel dimensions from physical size."""
    # Convert mm to inches
    width_inches = width_mm / 25.4
    height_inches = height_mm / 25.4

    # Calculate pixels
    width_px = int(width_inches * dpi)
    height_px = int(height_inches * dpi)

    return width_px, height_px

# Example: 180mm wide figure at 300 DPI
width, height = calculate_dimensions(180, 120, dpi=300)
print(f"Required resolution: {width}x{height} pixels")
# Output: Required resolution: 2126x1417 pixels
# R ggplot2 export with proper DPI
library(ggplot2)

# Create plot
p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point() +
  theme_minimal(base_size = 12)

# Save for publication (300 DPI)
ggsave(
  filename = "figure1.png",
  plot = p,
  width = 180,
  height = 120,
  units = "mm",
  dpi = 300
)

# Save as vector for flexibility
ggsave(
  filename = "figure1.pdf",
  plot = p,
  width = 180,
  height = 120,
  units = "mm",
  device = cairo_pdf  # Better text rendering
)

→ Graphics rendered at correct resolution for print quality If err: verify DPI metadata saved correctly, check file size appropriate

3. Configure Color Space

Set appropriate color profile:

from PIL import Image, ImageCms

def convert_to_cmyk(rgb_image_path, cmyk_output_path):
    """Convert RGB to CMYK for print."""
    img = Image.open(rgb_image_path)

    if img.mode != 'RGB':
        img = img.convert('RGB')

    # Convert to CMYK
    cmyk_img = img.convert('CMYK')
    cmyk_img.save(cmyk_output_path, format='TIFF', compression='tiff_lzw')
    print(f"Converted to CMYK: {cmyk_output_path}")

def apply_srgb_profile(image_path, output_path):
    """Apply sRGB profile for web."""
    img = Image.open(image_path)

    # sRGB profile (embedded in Pillow)
    srgb_profile = ImageCms.createProfile('sRGB')

    # Convert to sRGB
    img_srgb = ImageCms.profileToProfile(
        img,
        srgb_profile,
        srgb_profile,
        renderingIntent=ImageCms.Intent.PERCEPTUAL
    )

    img_srgb.save(output_path)
# ImageMagick for color space conversion
convert input.png -colorspace sRGB output_srgb.png
convert input.png -colorspace CMYK output_cmyk.tiff

# Check color profile
identify -verbose image.png | grep -i colorspace

→ Color space matches pub req If err: verify color profile embedded, test print preview

4. Configure Typography

Ensure text readable + properly formatted:

from PIL import ImageFont

def get_publication_fonts():
    """Load fonts appropriate for publication."""
    # Common publication-safe fonts
    fonts = {
        'serif': 'Times New Roman',
        'sans': 'Arial',
        'mono': 'Courier New'
    }

    try:
        # Load with proper size for DPI
        # At 300 DPI, 12pt = 12 * 300/72 = 50 pixels
        base_size_300dpi = 50

        font_regular = ImageFont.truetype(f"{fonts['sans']}.ttf", base_size_300dpi)
        font_bold = ImageFont.truetype(f"{fonts['sans']} Bold.ttf", base_size_300dpi)

        return {'regular': font_regular, 'bold': font_bold}
    except:
        return {'regular': ImageFont.load_default(), 'bold': ImageFont.load_default()}

# Typography guidelines
typography_specs = {
    'minimum_font_size': '8pt',  # Readable when printed
    'line_width_min': 0.5,  # Points, for print clarity
    'panel_labels': {
        'font': 'Arial Bold',
        'size': '12pt',
        'position': 'top-left',
        'style': 'A, B, C'  # Or (a), (b), (c)
    },
    'axis_labels': {
        'font': 'Arial',
        'size': '10pt'
    },
    'legend': {
        'font': 'Arial',
        'size': '9pt',
        'position': 'outside plot area'
    }
}
# R publication-quality typography
library(ggplot2)

p <- ggplot(mtcars, aes(x = wt, y = mpg)) +
  geom_point(size = 2) +
  labs(
    title = "Fuel Efficiency vs Weight",
    x = "Weight (1000 lbs)",
    y = "Miles per Gallon"
  ) +
  theme_bw(base_size = 12, base_family = "Arial") +
  theme(
    plot.title = element_text(size = 14, face = "bold"),
    axis.title = element_text(size = 12),
    axis.text = element_text(size = 10),
    legend.text = element_text(size = 10),
    panel.grid.minor = element_blank(),
    # Ensure text is black for print
    text = element_text(color = "black")
  )

→ Text readable at pub size, fonts embedded properly If err: increase font sizes, check font licensing, convert text to outlines

5. Select File Format

Choose by use case:

def export_multi_format(source_path, output_base, formats=['png', 'pdf', 'tiff']):
    """Export graphic in multiple formats."""
    from PIL import Image
    import cairosvg
    import os

    base, ext = os.path.splitext(output_base)

    if ext.lower() in ['.svg']:
        # SVG source - convert to rasters
        for fmt in formats:
            output = f"{base}.{fmt}"

            if fmt == 'png':
                cairosvg.svg2png(
                    url=source_path,
                    write_to=output,
                    output_width=2126,  # 180mm @ 300 DPI
                    output_height=1417   # 120mm @ 300 DPI
                )
            elif fmt == 'pdf':
                cairosvg.svg2pdf(url=source_path, write_to=output)
            elif fmt == 'tiff':
                # Convert via PNG intermediate
                temp_png = f"{base}_temp.png"
                cairosvg.svg2png(url=source_path, write_to=temp_png)
                img = Image.open(temp_png)
                img.save(output, format='TIFF', compression='tiff_lzw')
                os.remove(temp_png)

    else:
        # Raster source
        img = Image.open(source_path)

        for fmt in formats:
            output = f"{base}.{fmt}"

            if fmt == 'png':
                img.save(output, format='PNG', dpi=(300, 300), optimize=True)
            elif fmt == 'tiff':
                img.save(output, format='TIFF', compression='tiff_lzw', dpi=(300, 300))
            elif fmt == 'pdf':
                # Use img2pdf or similar for raster-to-PDF
                img.save(output, format='PDF', resolution=300.0)

    print(f"Exported in formats: {', '.join(formats)}")

# Format selection guide
format_guide = {
    'TIFF': {
        'use_for': 'Journal submission, archival',
        'benefits': 'Lossless, supports CMYK, high quality',
        'compression': 'LZW or ZIP (lossless)'
    },
    'PDF': {
        'use_for': 'Submission, print, archival',
        'benefits': 'Vector or raster, text searchable, widely accepted',
        'variants': 'PDF/A (archival), PDF/X (print)'
    },
    'PNG': {
        'use_for': 'Web, presentations, digital',
        'benefits': 'Lossless, transparency, good compression',
        'limitation': 'RGB only, larger than JPEG'
    },
    'SVG': {
        'use_for': 'Web, further editing, scalable graphics',
        'benefits': 'Vector, infinitely scalable, small file size',
        'limitation': 'Not always accepted by journals'
    },
    'EPS': {
        'use_for': 'Legacy journal requirements',
        'benefits': 'Vector format accepted by older systems',
        'limitation': 'Being phased out, use PDF instead'
    }
}

→ Appropriate format for pub channel If err: check pub req, provide multi formats

6. Optimize for Web

Create web-optimized vers:

def optimize_for_web(input_path, output_path, max_width=1200, quality=85):
    """Optimize image for web publication."""
    from PIL import Image

    img = Image.open(input_path)

    # Resize if too large
    if img.width > max_width:
        ratio = max_width / img.width
        new_height = int(img.height * ratio)
        img = img.resize((max_width, new_height), Image.LANCZOS)

    # Convert to RGB if needed
    if img.mode in ('RGBA', 'LA', 'P'):
        background = Image.new('RGB', img.size, (255, 255, 255))
        if img.mode == 'P':
            img = img.convert('RGBA')
        background.paste(img, mask=img.split()[-1] if 'A' in img.mode else None)
        img = background

    # Save optimized
    img.save(output_path, format='JPEG', quality=quality, optimize=True, progressive=True)

    # Check file size
    import os
    file_size_kb = os.path.getsize(output_path) / 1024
    print(f"Optimized: {file_size_kb:.1f} KB")

def create_responsive_set(input_path, output_base):
    """Create multiple resolutions for responsive web."""
    from PIL import Image

    img = Image.open(input_path)
    sizes = [
        (640, '640w'),
        (1024, '1024w'),
        (1920, '1920w')
    ]

    for width, suffix in sizes:
        if img.width >= width:
            ratio = width / img.width
            height = int(img.height * ratio)
            resized = img.resize((width, height), Image.LANCZOS)

            output = f"{output_base}_{suffix}.jpg"
            resized.save(output, format='JPEG', quality=85, optimize=True)

→ Web-optimized images < 500KB, responsive sizes generated If err: reduce quality, resize further, consider WebP format

7. Embed Metadata

Add descriptive metadata for archival:

from PIL import Image
from PIL.PngImagePlugin import PngInfo

def embed_metadata(image_path, output_path, metadata):
    """Embed metadata in PNG."""
    img = Image.open(image_path)

    # Create metadata
    png_info = PngInfo()
    for key, value in metadata.items():
        png_info.add_text(key, str(value))

    # Save with metadata
    img.save(output_path, format='PNG', pnginfo=png_info)

# Example metadata
metadata = {
    'Title': 'Figure 1: Relationship between weight and fuel efficiency',
    'Author': 'Jane Doe',
    'Description': 'Scatter plot showing negative correlation',
    'Copyright': 'CC-BY 4.0',
    'Software': 'R 4.3.0, ggplot2 3.4.0',
    'Creation Date': '2026-02-16',
    'Source': 'mtcars dataset'
}

embed_metadata('figure1.png', 'figure1_with_metadata.png', metadata)

→ Metadata embedded + retrievable If err: check format supports metadata (PNG, TIFF, PDF yes; JPEG limited)

Check

  • DPI meets pub req (typically 300+)
  • Physical dims correct for pub
  • Color space appropriate (RGB web, CMYK print)
  • Format accepted by publisher
  • Text readable at pub size
  • Fonts embedded or outlined
  • Line widths visible printed
  • Color contrast sufficient grayscale
  • File size in limits
  • Metadata embedded
  • Tested print preview or rendering

Traps

  1. Insufficient resolution: 72 DPI web cannot print at quality
  2. Wrong color space: RGB may print diff than displayed
  3. Font substitution: Non-embedded fonts replaced w/ defaults
  4. Small text: Fonts < 8pt may be illegible printed
  5. Thin lines: Lines < 0.5pt may not print clearly
  6. File size: High DPI very large, compress appropriately
  7. Compression artifacts: JPEG unsuitable for line art or text
  8. Missing bleed: Print needs 3-5mm bleed beyond trim
  9. Transparency issues: Some formats don't preserve correctly
  10. Aspect ratio: Distortion from incorrect dimension calc

GitHub Repository

pjt222/agent-almanac
Path: i18n/caveman-ultra/skills/render-publication-graphic
0
agentsagentskillsai-assisted-developmentclaude-codeskillsteams

Related Skills

executing-plans

Design

Use the executing-plans skill when you have a complete implementation plan to execute in controlled batches with review checkpoints. It loads and critically reviews the plan, then executes tasks in small batches (default 3 tasks) while reporting progress between each batch for architect review. This ensures systematic implementation with built-in quality control checkpoints.

View skill

requesting-code-review

Design

This skill dispatches a code-reviewer subagent to analyze code changes against requirements before proceeding. It should be used after completing tasks, implementing major features, or before merging to main. The review helps catch issues early by comparing the current implementation with the original plan.

View skill

connect-mcp-server

Design

This skill provides a comprehensive guide for developers to connect MCP servers to Claude Code using HTTP, stdio, or SSE transports. It covers installation, configuration, authentication, and security for integrating external services like GitHub, Notion, and custom APIs. Use it when setting up MCP integrations, configuring external tools, or working with Claude's Model Context Protocol.

View skill

web-cli-teleport

Design

This skill helps developers choose between Claude Code Web and CLI interfaces based on task analysis, then enables seamless session teleportation between these environments. It optimizes workflow by managing session state and context when switching between web, CLI, or mobile. Use it for complex projects requiring different tools at various stages.

View skill