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-hypermotiongit 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-hypermotion)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-hypermotion"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-hypermotion/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-hypermotion"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-hypermotion.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 | $0.00182 | $0.01365 |
| Opus 5 | $0.00091 | $0.00682 |
| Sonnet 5 | $0.00036 | $0.00273 |
| Haiku 4.5 | $0.00018 | $0.00136 |
Grade A, and why
render-hypermotion 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 13d 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 — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-hypermotion
Assembles a product-hypermotion + kinetic-typography ad: a vertical 9:16 sizzle (~20–30s) where ONE spectacular AI-gen hypermotion clip of the hero product carries the energy and punchy PIL-rendered spec cards carry the facts. Music-led, no VO — it reads as a high-production sizzle, not UGC.
The reusable IP is one-call-many-cuts + intercut: dice ONE 12–15s Seedance 2.0 hypermotion i2v into 5–6 segments (never a paid call per segment) and interleave PIL kinetic-typography spec cards between the cuts, capped by a real-logo end card, over a 124 BPM bass bed.
This capability ships the FREE, deterministic assembly — everything between the two paid
model calls. It is documentation-grade + config: the format has no runnable driver.
scripts/ carries the worked config + the step-by-step pipeline doc; the Soundboks
reference PIL impls (gen_kinetic_v6.py, gen_endcard_v10.py, assemble_v10.py) are
copied + adapted per run, not vendored.
- config.example.json — the Soundboks worked example: hero product, 5 spec callouts,
the 5-block Seedance prompt, per-card treatments, beat structure, end-card spec, music
brief, dims. Copy to
config.jsonand edit. - PIPELINE.md — the full config-field → step map: Phase 0 assets → Phase 1 PAID Seedance + music (parallel, gated) → Phase 2 FREE PIL cards → Phase 3 FREE dice/intercut/concat/mux → Phase 4 watch/QC. Names the atom/tool each step uses, plus 20s/25s/30s beat-structure variants.
- README.md — documents the FREE assembly (below).
The FREE assembly
- PIL kinetic-typography spec/CTA cards — full-frame 1080×1920 clips rendered frame-by-frame on a dark grain BG with the static Space Grotesk Bold TTF: italic skew (~6°), outline echo at 1.08× (bleed-safe), 3D extrusion (hero stat + CTA, side = brand accent), slam-with-shake, and color/inversion flash. No VO — the cards carry every fact.
- Real-logo PIL end card — the brand's actual logo PNG (base64-decoded out of the SVG, never typeset) with a slam-motion-blur entry, settle, continuous micro-motion (±1% scale + ±3px drift for the full hold — never freeze), an inversion flash at ~60%, and a cascade reveal of the spec-dot subtitle + CTA.
- FFmpeg dice + intercut — center-crop the ONE hypermotion clip 1:1 → 9:16, dice into 5–6 decreasing-length segments at Seedance's natural beats (crash-zoom → orbit → settle), then concat in the fixed intercut order: open on the intro card, alternate segment ↔ spec card, end on the CTA + brand end card.
- Explicit-map mux — mux the music bed as a SEPARATE pass with
-map 0:v -map 1:a(the default mapping silently ships ~1 kbps garbage audio; verifyffprobe≈ 192 kbps).
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.
- 13d ago First seen · 78 lines · 182 tokens per session scan A 76146a7e5653
render-hypermotion is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 182 tokens to every session and 1,365 once invoked, about $0.0009 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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