Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add oaustegard/claude-skills --skill image-to-svggit clone --depth 1 https://github.com/oaustegard/claude-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/oaustegard/claude-skills/image-to-svg)<a href="https://agentmods.dev/skills/oaustegard/claude-skills/image-to-svg"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/image-to-svg/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/oaustegard/claude-skills/image-to-svg"><img src="https://agentmods.dev/badge/skills/oaustegard/claude-skills/image-to-svg.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00122 | $0.05014 |
| Opus 5 | $0.00061 | $0.02507 |
| Sonnet 5 | $0.00024 | $0.01003 |
| Haiku 4.5 | $0.00012 | $0.00501 |
Grade A, and why
image-to-svg scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(['rsvg-convert', '-w', '1400', 'output.svg', '-o', 'output.png']) How it starts
The opening of the file, as written. The whole thing — 358 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Image to SVG Reproduction
Convert raster images into faithful SVG reproductions using data-driven color quantization and contour extraction. Never hand-draw shapes from visual interpretation — always extract geometry from the actual pixel data.
Core Principle
Trust the data, not your imagination. Claude's visual interpretation of images is unreliable for precise color matching, shape positioning, and spatial relationships. Every shape, color, and position must come from computational analysis of the source pixels.
Quick Start
pip install opencv-python-headless scikit-image scipy scikit-learn --break-system-packages -q
apt-get install -y librsvg2-bin -qq
import sys
sys.path.insert(0, '/mnt/skills/user/image-to-svg/scripts')
from pipeline import image_to_svg
svg, flow = image_to_svg("source.jpg", mode="painting")
with open("output.svg", "w") as f:
f.write(svg)
flow.summary() # timing + status per step
Mode Selection
Look at the image and ask: "Does this have smooth gradients or hard edges?" Gradients → higher K. Hard edges → lower K.
| Mode | K | Best for | Dark shape gating |
|---|---|---|---|
"graphic" |
28 | Logos, icons, Kandinsky, flat design | Loose (keeps thin lines) |
"illustration" |
40 | Comics, editorial, digital art | Moderate |
"painting" |
56 | Renaissance, Impressionist, watercolor | Standard |
"photo" |
64 | Portraits, landscapes, still life | Standard (prevents woodcut artifacts) |
Default is "painting". When uncertain, start there.
Tradeoffs: K=64 produces ~2300 shapes (~1.2MB SVG) vs K=28's ~1000 shapes (~550KB). Processing time roughly doubles with K. The quality gain in tonal gradation is substantial for photos but wasted on graphic art.
All mode defaults (K, dark_lum, compactness_min, etc.) can be overridden via **kwargs:
svg, flow = image_to_svg("source.jpg", mode="graphic", K=12, min_area=20)
Compositional Pipeline (Line Art)
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 358 lines · 122 tokens per session scan A df386cbab388
image-to-svg is a skill published in the GitHub repository oaustegard/claude-skills (148 stars, last pushed yesterday), licensed MIT. It adds 122 tokens to every session and 5,014 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
superdesign
Design or redesign frontend UI, presentations, and graphics on the Superdesign canvas with a choice of leading AI models. Use whenever the user wants to design a page, feature, flow, slide deck, or brand-new product; improve or reproduce existing UI; compare design results across top models; explore visual variants…
high-quality-slides
A method for creating polished presentation slides, including HTML slide decks, with research, a clear story, chosen visuals, and fixed layouts.
data-craft
Generate beautiful themed HTML data displays. Six visual idioms with matched voices, rendered as self-contained HTML files. Use when the user asks for charts, dashboards, reports, tearsheets, briefings, data visualizations, or any data-driven document.
tufte-viz
Design, build, and critique data visualizations using Edward Tufte's principles. Use this skill WHENEVER the work involves a chart, graph, plot, dashboard, table, diagram, or any visual display of data, even if the user does not name Tufte or "data viz" explicitly. Triggers include: (1) Building a new chart, graph…
figma-to-code
Generate production-ready React/Next.js code from Figma designs. Instructs proper Figma MCP tool usage, component reuse strategies, and variant mapping. CRITICAL: ALWAYS use this skill when user provides a Figma link or mentions implementing/redesigning from Figma design. Never attempt manual implementation without…
wp-ux-design
WordPress UX and design enforcement — Core Web Vitals, mobile-first layout, typography, color systems, navigation, page builder patterns, image optimization, form UX, loading and error states, admin UX, and performance checklists with concrete CSS/HTML/PHP examples.