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-flat-vector-explainergit 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-flat-vector-explainer)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-flat-vector-explainer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-flat-vector-explainer/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-flat-vector-explainer"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-flat-vector-explainer.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 | $0.00209 | $0.01227 |
| Opus 5 | $0.00105 | $0.00613 |
| Sonnet 5 | $0.00042 | $0.00245 |
| Haiku 4.5 | $0.00021 | $0.00123 |
Grade A, and why
render-flat-vector-explainer 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-flat-vector-explainer
Assembles a flat-vector product-routine explainer: one illustrated creator-character walks through a countable N-step routine (e.g. collagen -> serum -> eye cream -> hair), one step per beat, each beat carrying a large corner numeral, a labelled chip + one-line tagline, and the step's real product photo, closing on an "N products" grid + brand CTA. It reads as a premium DTC explainer (Spotify/Anchor flat-vector lineage), not UGC.
This capability is documentation-grade. The content-goose molecule is a documented recipe, not a runnable end-to-end app, so this capability ships the config schema (scripts/config.example.json), the field-to-script map (scripts/PIPELINE.md), and a README (scripts/README.md) describing the FREE assembly steps the agent runs by hand with ffmpeg + Remotion + PIL. The paid generative steps are separate capabilities the recipe orchestrates and gates.
The two non-negotiable separations
- Motion layer != text layer. Animate a text-stripped clean plate with Kling i2v (subtle motion, style-preserving negative, cfg 0.5), then composite every chip / numeral / tagline / slate / CTA as an animated Remotion DOM overlay on top. Baking text into the keyframe before i2v warps the type and forfeits the ability to retime/restyle it — this separation is the format's whole credibility.
- Real assets != AI assets. The per-step product photo and the closing "N products" grid are real product webps composited with PIL (AI duplicates SKUs in a grid). Only the character vignettes and stylized backgrounds are generative.
Free assembly steps (this capability)
The agent runs these deterministic, $0 steps by hand — see scripts/README.md for the ffmpeg/Remotion/PIL detail:
- Remotion overlay — import each Kling clip as the moving base; composite chips / numerals / taglines / slate / grid / CTA as animated DOM on top -> the animated silent master. Slate/grid/CTA beats are Remotion text with no i2v.
- PIL product grid — composite the N real product webps on the brand ground for the closing lockup; preserve each aspect (never stretch, never AI-dupe).
- Captions — word-by-word burned from the eleven_v3 with-timestamps char timings (libass); suppress on slate/grid/CTA scenes so two text layers don't collide.
- Audio mix + master — place each VO line at its scene start, duck the music under VO (sidechaincompress),
loudnorm I=-15VO-forward, mux, burn captions LAST ->finals/master-final.mp4(~50s). - 30s cut — slice each beat's region OUT of the animated silent master (never a static intermediate); trim short beats, gently slow long beats (setpts <=1.6x), re-burn scaled captions ->
finals/master-final-30s-v1.mp4.
What ships with it
5 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.
- 9d ago First seen · 45 lines · 209 tokens per session scan A 3151515e5d24
render-flat-vector-explainer is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,199 stars, last pushed 8d ago), licensed MIT. It adds 209 tokens to every session and 1,227 once invoked, about $0.0010 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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