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-glassy-matte-grwmgit 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-glassy-matte-grwm)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-glassy-matte-grwm"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-glassy-matte-grwm/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-glassy-matte-grwm"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-glassy-matte-grwm.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 | $0.00233 | $0.01479 |
| Opus 5 | $0.00117 | $0.00740 |
| Sonnet 5 | $0.00047 | $0.00296 |
| Haiku 4.5 | $0.00023 | $0.00148 |
Grade A, and why
render-glassy-matte-grwm 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 — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-glassy-matte-grwm
Assemble a multi-scene GRWM beauty-demo ad from a config — a locked-identity creator applies ~5 makeup/skincare products step by step at a vanity, a separate ElevenLabs voiceover narrates the routine, and every scene cut is snapped to the VO's product-name word-starts, with ~5 Playwright product overlay cards on the product-name beats, a ducked music bed, burned captions, and a flat-lay end card. This capability is the FREE, deterministic assembly — the Whisper-driven re-cut + hard-concat, the Playwright card render + card composite, the VO + music mix, the caption burn, and the flat-lay end card.
This is the multi-scene beauty demo, distinct from the single-take apparel outfit-reveal
(ugc-grwm, one Seedance reference-to-video call with native lip-sync and minimal post). Here the
timeline is driven by a SEPARATE VO and the scenes are re-cut to its word-starts.
scripts/config.example.json is the worked example (DIBS Beauty "5-Step Glassy Matte Routine",
~32s 1080×1920 9:16, 12 VO-snapped cuts + 5 product cards); scripts/PIPELINE.md maps every
config block to its source step and scripts/README.md documents the free assembly.
Run
This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are
separate capabilities — the SEPARATE narration VO (create-music-elevenlabs, or a user-supplied
mp3; word-level Whisper timestamps set the timeline), ~7 Seedance scene clips one per product step
(create-video-fal), the ~5 white-bg product cutouts + the flat-lay end-card still
(create-image-gpt-image-fal), and the ducked music bed. Given the VO + .words.json + one clip
per step + the ~5 product cutouts + the music bed, render-glassy-matte-grwm re-cuts each clip to
its VO word-start window, hard-concats on the cut, renders + composites the product cards on the
product-name beats, mixes the VO over the ducked music, burns the captions, and appends the
flat-lay end card → the master. Re-cuts reuse the existing VO / clips / cutouts and cost $0.
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 · 68 lines · 233 tokens per session scan A d72c554cb17a
render-glassy-matte-grwm is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 233 tokens to every session and 1,479 once invoked, about $0.0012 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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