Scientific Figure QA
Use this skill when a user asks whether a figure is ready for a manuscript,
conference paper, journal submission, poster, rebuttal, or camera-ready
package.
Checks
- Color accessibility: color vision deficiency, grayscale distinguishability,
contrast on white backgrounds, and redundant marker/line encodings.
- Palette provenance: confirm canonical, journal-inspired non-official,
external GPL, or Notion-derived experimental status.
- Typography and layout: final-size font legibility, axis labels with units,
legend placement, panel labels, consistent spacing, and annotation clarity.
- Statistical visual integrity: uncertainty/error bars, significance markers,
colorbar labels, centered diverging maps only for data with a real midpoint.
- Export readiness: vector output for line art, 300+ dpi raster output,
transparent-background risks, and journal/venue file-format constraints.
Palette QA
For palette-specific checks, use the palette CLI rather than inventing color
metadata:
python scripts/palette.py get okabe_ito --format json
python scripts/palette.py recommend --figure-type umap --paper-venue Nature --style-source all --explain
Do not promote Notion-derived candidates to reviewed status during QA unless a
separate manual review explicitly records the review evidence.
1---2name: scientific-figure-qa3description: Audit scientific figures for publication readiness, accessibility, grayscale readability, export quality, and venue visual constraints.4---5# Scientific Figure QA67Use this skill when a user asks whether a figure is ready for a manuscript,8conference paper, journal submission, poster, rebuttal, or camera-ready9package.1011## Checks1213- Color accessibility: color vision deficiency, grayscale distinguishability,14 contrast on white backgrounds, and redundant marker/line encodings.15- Palette provenance: confirm canonical, journal-inspired non-official,16 external GPL, or Notion-derived experimental status.17- Typography and layout: final-size font legibility, axis labels with units,18 legend placement, panel labels, consistent spacing, and annotation clarity.19- Statistical visual integrity: uncertainty/error bars, significance markers,20 colorbar labels, centered diverging maps only for data with a real midpoint.21- Export readiness: vector output for line art, 300+ dpi raster output,22 transparent-background risks, and journal/venue file-format constraints.2324## Palette QA2526For palette-specific checks, use the palette CLI rather than inventing color27metadata:2829```bash30python scripts/palette.py get okabe_ito --format json31python scripts/palette.py recommend --figure-type umap --paper-venue Nature --style-source all --explain32```3334Do not promote Notion-derived candidates to reviewed status during QA unless a35separate manual review explicitly records the review evidence.
YuukiAS