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 render-vignettegit 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/render-vignette)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-vignette"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-vignette/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/render-vignette"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-vignette.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 | $0.00099 | $0.01087 |
| Opus 5 | $0.00049 | $0.00544 |
| Sonnet 5 | $0.00020 | $0.00217 |
| Haiku 4.5 | $0.00010 | $0.00109 |
Grade A, and why
render-vignette 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 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.
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 — 31 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-vignette
Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video. Motion lives in the BG video; the product rides on top as a static cutout layer. Music-led, zero VO, sub-12s, loopable — it reads muted because the product label + on-screen copy carry the message. Defaults to the V-CARD structure: a cold-open text card → a product carousel under one shared BG → an annotated specimen-sheet end card.
Run
strip_product_backgrounds.py— birefnet cutout of each PDP shot to clean hard-edge alpha (no halo/shadow).render_overlays.py— PIL +rsvg-convertrender the cold-open card (Boska Black, dead-center) + the annotated specimen-sheet end card (brand SVG logo + Space Grotesk annotations) as transparent 1080x1920 PNGs. FREE.composite_variants.py— one FFmpegfilter_complexper BG variant: BG (palette-aware dim) → cold-open overlay → cutouts (width-anchored, vertically centeredy=(H-h)/2) → end card. h264 crf20 yuv420p +faststart 30fps. FREE.music_and_mux.py— instrumental music bed →acompressor + loudnorm I=-18:TP=-2:LRA=9→ muxed into every variant in a SEPARATE pass with explicit-map 0:v:0 -map 1:a:0. The mux is FREE; the music generation is a paid call that in prod routes through create-music-elevenlabs.
Contract
- FREE assembly: birefnet cutout (see gap below) + PIL/rsvg overlays + FFmpeg composite + mux. No AI-rendered text; the product art/labels and on-screen copy are real, never invented.
- The template recipe (DB) supplies the per-brand config (products, cold-open text, end-card lines, BG concept, beat timing). This capability is the generic assembler.
- Craft rules preserved from the source molecule:
- Cutouts stripped clean (no halo/shadow), height-anchored at vertical-center (
y=(H-h)/2) so mixed-shape SKUs share one visual mid-line — never bottom-anchor (squat jars jump). For 9:16 scale by WIDTH (~75% tall bottles, ~65% squat jars). - Palette-aware BG dim: high-contrast/chrome BG → push saturation DOWN hard (
saturation=0.50); naturally-contrasty BG → lighter dim (saturation=0.85). - End card = annotated specimen-sheet (EST year + rule + wordmark + rule + ingredient + positioning + claim), never a bare logo. Use the WHITE logo variant on dark BGs, cream on light.
- Music-led, NO VO — instrumental only (VO/lyrics would fight the cold-open + end-card text). Loudnorm before the mux.
- Mux is a SEPARATE FFmpeg pass with explicit
-map 0:v:0 -map 1:a:0(single-pass composite+mux silently ships 1 kbps garbage audio).
- Cutouts stripped clean (no halo/shadow), height-anchored at vertical-center (
What ships with it
6 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 · 31 lines · 99 tokens per session scan A 54d2fa8c5970
render-vignette is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,201 stars, last pushed 10d ago), licensed MIT. It adds 99 tokens to every session and 1,087 once invoked, about $0.0005 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 2026-08-30.
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