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dkyazzentwatwa/chatgpt-skills188 installs

svg-precision

Deterministic SVG generation, validation, and rendering. Use for icons, diagrams, charts, UI mockups, or technical drawings requiring structural correctness and cross-viewer compatibility.

How do I install this agent skill?

npx skills add https://github.com/dkyazzentwatwa/chatgpt-skills --skill svg-precision
view source ↗

Is this agent skill safe to install?

  • Gen Agent Trust Hubpass

    The svg-precision skill is a legitimate and well-structured utility for deterministic SVG generation, validation, and rendering. It uses standard, reputable Python libraries (CairoSVG, Pillow) and does not contain any malicious code, obfuscation, or unauthorized data access patterns.

  • Socketpass

    No alerts

  • Snykpass

    Risk: LOW · No issues

  • Runlayerwarn

    15/15 files flagged

What does this agent skill do?

svg-precision

Generate structurally correct SVGs from a strict JSON spec (scene graph), then validate and optionally render a PNG preview.

Fast path

  1. Turn the user request into a Spec JSON (use templates in references/spec.md).
  2. Build the SVG:
    • python scripts/svg_cli.py build spec.json out.svg
  3. Validate:
    • python scripts/svg_cli.py validate out.svg
  4. (Optional) Render a PNG preview (requires CairoSVG):
    • python scripts/svg_cli.py render out.svg out.png --scale 2

Spec design rules (for accuracy)

  • Always set canvas.viewBox and explicit canvas.width/canvas.height.
  • Prefer absolute coordinates; use transforms only when they reduce complexity.
  • Keep numbers sane: no NaN/inf; round to 3-4 decimals.
  • Put reusable items in defs (markers, gradients, clipPaths) and reference by id.
  • For predictable results across viewers, avoid exotic filters unless required.
  • Text varies by fonts/viewers. If you need pixel-identical results, treat text as a risk and prefer shapes.

Using the bundled scripts

CLI

  • python scripts/svg_cli.py build <spec.json> <out.svg>
  • python scripts/svg_cli.py validate <svg>
  • python scripts/svg_cli.py render <svg> <out.png> [--scale N]
  • python scripts/svg_cli.py diff <a.svg> <b.svg> <diff.png> [--scale N] (renders + image-diffs)

As a library (in Python)

from svg_skill import build_svg, validate_svg
svg_text = build_svg(spec_dict)
report = validate_svg(svg_text)

When the request is vague

  1. Identify the kind of SVG: icon / diagram / chart / UI / technical.
  2. Pick a template from references/spec.md and fill in concrete numbers.
  3. If dimensions are unknown, choose defaults that match the domain:
    • icons: 24x24 or 32x32
    • UI mockups: 1440x900 or 390x844 (mobile)
    • charts: 800x450
    • diagrams: 1200x800
    • technical drawings: specify units (mm/in) and scale

References

  • references/spec.md - schema + ready-to-copy templates per SVG type
  • references/recipes.md - layout and styling patterns that render consistently

Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.

<a href="https://skillzs.dev/skills/dkyazzentwatwa/chatgpt-skills/svg-precision">View svg-precision on skillZs</a>