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name: concept-manifold-diagrams description: "Create publication-ready conceptual scientific diagrams with scatter clouds, KDE contours, manifold curves, Swiss-roll diffusion matrices, shaded spheres, geodesic arrows, and 3D projection arrows in the figures4papers style." disable-model-invocation: true metadata: disco-role: operating license: NO_LICENSE

Concept Manifold Diagrams

Use this sub-skill when the requested figure is a conceptual diagram rather than a quantitative result plot: concept distributions, latent/manifold geometry, diffusion or transition structure, sphere/geodesic illustrations, or projection/repulsion arrows.

Route here for

• VIGIL-style concept distributions: overlapping probability curves, highlighted target gaps, and a paired conceptual manifold panel.
• Cflows-style Swiss-roll or diffusion visuals: ordered transition/probability matrices beside manifold point clouds and local connections.
• Dispersion-style geometry diagrams: shaded 2D spheres, sampled points, radial guides, geodesic arcs, directional arrows, and 3D projection/repulsion arrows.
• RNAGenScape-style manifolds: smooth synthetic energy surfaces, multi-well landscapes, and hole/constraint overlays.

Route elsewhere

• Ordinary quantitative bars, grouped comparisons, horizontal ablations, value annotations, or legend-only bar panels: use bar-comparison-figures.
• Radar charts, trend/line panels, event timelines, heatmaps, or matrix result comparisons: use trend-radar-heatmap-figures.
• Cross-cutting palette, export, environment, or shared helper concerns: use the root figures4papers references/scripts once available.

Operating workflow

1. Decide whether the diagram is synthetic explanatory geometry or a data-backed result plot. This sub-skill is for the former; do not imply synthetic samples are measured results.
2. Read references/concept-manifold-recipes.md for the recipe matching the requested visual family:
   • probability/distribution panels,
   • scatter clouds with KDE contours and manifold ridges,
   • Swiss-roll plus diffusion matrix,
   • 3D manifold and hole surfaces,
   • shaded spheres, geodesic arcs, and projection arrows.
3. Run or adapt scripts/concept_manifold_template.py when a safe starting script is useful. It is self-contained, deterministic, headless-friendly, and creates figures from built-in synthetic data:
   • python scripts/concept_manifold_template.py --example distribution --output concept_distribution
   • python scripts/concept_manifold_template.py --example manifold --output manifold_surface --formats png,pdf
   • python scripts/concept_manifold_template.py --example swiss-roll --output swiss_roll
   • python scripts/concept_manifold_template.py --example sphere --output sphere_geometry
4. Before final export, apply the validation checklist in the recipes: deterministic seed, finite sample/grid sizes, normalized probability rows where applicable, visible arrows/text, clean axes, and PNG/PDF output at publication DPI.
5. If SciPy, KDE, matrix normalization, TeX labels, 3D arrows, or layout fail, read references/troubleshooting.md before changing the figure design.

Style expectations

• Use the figures4papers house look: minimalist axes, large sans-serif labels, blue/neutral/red semantic palette, white backgrounds, dense-but-readable panels, and 300 DPI exports.
• Prefer annotations, arrows, and legend-only cues over heavy grids for conceptual diagrams.
• Keep stochastic visuals reproducible with explicit seeds and note that all generated template examples are illustrative synthetic data.