Assemble a wordless macro-tabletop food-product sizzle ad from a config — normalize fps and SAR across ~4 photorealistic macro clips (hands tearing, flat lay, bite, box hero), concat them, apply a global anti-AI grain pass (eq plus hqdn3d plus noise), composite the audio (a non-diegetic acoustic music bed plus a couple of short diegetic SFX like a snap and a tear placed at measured cue points, loudnorm), composite a STATIC end card entirely in PIL (real logo PNG plus real product PNG plus a serif heritage headline plus a CTA — never AI-rendered text), and burn optional serif stat-callout pills at beats. This is the FREE deterministic assembly stage (normalized concat plus grain plus music and SFX mix plus PIL end card plus callouts); the macro keyframes, i2v clips, and music bed come from create-image-fal, create-video-fal, and create-music-elevenlabs. Use for the food-product-sizzle format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill 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.
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.
bed; do not add a spoken voiceover. The brand name + claim land on the STATIC end card, never in
the body.
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).
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.
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.
trims the ~2.5s intro so it kicks in from frame 0; the assembly loudnorms + fades in / out to the
master length.
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 · ).
choreographed windows. Write any % string to a textfile and use ffmpeg drawtext textfile= +
expansion=none — a raw % is read as a strftime spec and renders garbage.
(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.
Create beautiful visual art in .png and .pdf documents using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.
Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.
Lightweight WSI tile extraction and preprocessing. Use for basic slide processing tissue detection, tile extraction, stain normalization for H&E images. Best for simple pipelines, dataset preparation, quick tile-based analysis. For advanced spatial proteomics, multiplexed imaging, or deep learning pipelines use pathml.
Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Take gooseworks-ai/render-food-product-sizzle from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.