svg-precision — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited svg-precision (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Generate structurally correct SVGs from a strict JSON spec (scene graph), then validate and optionally render a PNG preview.
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.svg3) Validate:
python scripts/svg_cli.py validate out.svg4) (Optional) Render a PNG preview (requires CairoSVG):
python scripts/svg_cli.py render out.svg out.png --scale 2canvas.viewBox and explicit canvas.width/canvas.height.defs (markers, gradients, clipPaths) and reference by id.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)from svg_skill import build_svg, validate_svg
svg_text = build_svg(spec_dict)
report = validate_svg(svg_text)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:
references/spec.md - schema + ready-to-copy templates per SVG typereferences/recipes.md - layout and styling patterns that render consistently~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.