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-food-product-sizzlegit 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-food-product-sizzle)<a href="https://agentmods.dev/skills/gooseworks-ai/goose-skills/render-food-product-sizzle"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-food-product-sizzle/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-food-product-sizzle"><img src="https://agentmods.dev/badge/skills/gooseworks-ai/goose-skills/render-food-product-sizzle.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 | $0.00218 | $0.01289 |
| Opus 5 | $0.00109 | $0.00645 |
| Sonnet 5 | $0.00044 | $0.00258 |
| Haiku 4.5 | $0.00022 | $0.00129 |
Grade A, and why
render-food-product-sizzle 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 — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.
render-food-product-sizzle
Assemble a food-product sizzle ad from a config: a wordless macro-tabletop photorealistic sizzle for a physical food / CPG product — tactile sunlit tabletop photography in a warm tungsten kitchen register, ~4 dynamic macro scenes (hands tearing, a flat lay, a partial-face bite, a box / pack hero) flowing into a static end card, carried by a non-diegetic acoustic music bed + a few diegetic SFX with NO voiceover. This capability is the FREE, deterministic assembly — normalized concat, the anti-AI grain pass, the audio (music bed + SFX) composite, the PIL end card, and the optional serif stat-callout pills.
scripts/config.example.json is the worked example (Lineage Provisions "Beef Sticks Sizzle", ~14s
1080×1920 9:16, ~4 macro scenes + a static end card); 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: ~4 photographic macro keyframes (create-image-fal, Nano Banana; the box / pack hero
grounds on the real product PNG); one locked-off, anti-shake i2v clip per keyframe (create-video-fal,
Seedance); and a non-diegetic acoustic / bluegrass bed (create-music-elevenlabs). Given the ~4
clips + the music bed + the diegetic SFX + the real logo PNG + the real product PNG,
render-food-product-sizzle normalizes fps / SAR, concats the body clips, applies the anti-AI grain
pass, composites the audio (bed + SFX at their cue points), composites the static PIL end card, burns
the optional serif callout pills, and muxes → the master. Re-cuts reuse the existing keyframes /
clips / music and cost $0.
Contract (the free assembly)
- Wordless — the music bed is the audio, no VO. A non-diegetic acoustic bed (no vocals) IS the bed; do not add a spoken voiceover. The brand name + claim land on the STATIC end card, never in the body.
- ~4 macro scenes, concat in order. Normalize fps / SAR across the body clips and concat them in their scene order (tear → flat-lay → bite → box-hero by default); the box / pack hero shows the REAL label (grounded on the product PNG upstream — the assembly must not re-render it).
- Anti-AI grain pass, applied globally. Apply
eq=contrast=1.06:saturation=0.93,hqdn3d=1.5:1.5:3:3,noise=alls=8:allf=t+uacross the whole video — the noise on a food macro is load-bearing for the tactile / photographic read, otherwise the sizzle looks AI-smooth. - Diegetic SFX on the tactile beats. Mix a crisp ~120ms snap on the fiber tear and a ~180ms tear on the box-open at their measured cue points — a couple of short hits, not a wall of sound. Time each to its beat, not a round number.
- Music bed with no sparse intro. The supplied / generated bed opens sparse — the upstream step trims the ~2.5s intro so it kicks in from frame 0; the assembly loudnorms + fades in / out to the master length.
- Static end card via PIL from the real logo + product PNG — never AI-render brand text. Solid /
ivory bg + the real logo PNG (upper third) + the real product PNG (centered, soft shadow) + a serif
heritage headline + a CTA, held ~3s WITH the music still playing under it (fade the tail — no silent
tail). A diffusion model garbles a wordmark and the packaging. On macOS pick a serif with the
middle-dot glyph (use
·). - Optional serif stat-callout pills at beats. Ivory-or-brand-color pill + serif type at
choreographed windows. Write any
%string to a textfile and use ffmpegdrawtexttextfile=+expansion=none— a raw%is read as a strftime spec and renders garbage. - FFmpeg composite, deterministic, FREE. Concat the body clips, grain-pass, composite the audio
(bed + SFX), append the PIL end card, burn the callouts, mux with a fade tail,
loudnorm→ a 1080×1920 h264+aac master (~14s). No paid calls, no keys.
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 · 61 lines · 218 tokens per session scan A cff5d8fd47ab
render-food-product-sizzle is a skill published in the GitHub repository gooseworks-ai/goose-skills (1,202 stars, last pushed 11d ago), licensed MIT. It adds 218 tokens to every session and 1,289 once invoked, about $0.0011 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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