Goose Skills is a library of workflows and data APIs that lets coding agents handle growth and go-to-market work such as advertising, social media, content, SEO, lead generation, and customer research. It is intended for teams using Claude Code, Cursor, Codex, and similar agents. The catalogue entries are its reusable skills.
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 gooseworks-ai/goose-skills --skill visual-brand-extractorgit clone --depth 1 https://github.com/gooseworks-ai/goose-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/gooseworks-ai/goose-skills/visual-brand-extractor)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/visual-brand-extractor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/visual-brand-extractor/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/gooseworks-ai/goose-skills/visual-brand-extractor"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/visual-brand-extractor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00067 | $0.04207 |
| Opus 5 | $0.00034 | $0.02103 |
| Sonnet 5 | $0.00013 | $0.00841 |
| Haiku 4.5 | $0.00007 | $0.00421 |
Grade A, and why
visual-brand-extractor scanned grade A with 0 findings 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 9d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 406 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Visual Brand Extractor
Extract a client's visual identity from their website and generate reusable style presets for slides and content assets. This is an agent-executed skill — the AI reads pages via WebFetch and performs the analysis directly.
Quick Start
Extract visual branding from https://vapi.ai for the Vapi client.
Inputs
| Input | Required | Description |
|---|---|---|
| Website URL | Yes | Client's homepage or landing page URL |
| Client name | Yes | For naming the output files |
| Additional pages | No | Product page, docs page, etc. for richer extraction |
Output
Two files saved to clients/<client-name>/brand/visual-identity.md:
- Slide preset — CSS custom properties, typography, and signature elements matching the format in
skills/create-html-slides/STYLE_PRESETS.md - Brand config JSON — Simple color/font config matching
skills/content-asset-creatorformat
Process
Phase 1: Fetch Target Pages
Fetch 2-3 pages to get a representative sample of the brand:
- Homepage (mandatory) — the primary brand expression
- Product/feature page (if available) — deeper color and layout usage
- Blog or about page (optional) — secondary design context
Use WebFetch on each URL with a prompt like:
"Extract the full content of this page. I need: all color values (hex, rgb, hsl), font family names, CSS class names (especially Tailwind utility classes), any CSS custom properties/variables, meta tags, and the general structure of the page layout. Preserve exact color codes and font names."
Phase 2: Extract Color Palette
Analyze the fetched content to identify the color palette. Look for these sources in priority order:
2.1 CSS Custom Properties
Look for :root, html, or body blocks containing color variables:
--color-primary, --primary, --brand, --accent
--bg-*, --background-*
--text-*, --foreground-*
2.2 Meta Tags
Check for:
<meta name="theme-color" content="#...">— often the primary brand color<meta name="msapplication-TileColor" content="#...">
What ships with it
1 file 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.
- 9d ago First seen · 406 lines · 67 tokens per session scan A e720c18bedd2
visual-brand-extractor is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 67 tokens to every session and 4,207 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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