Assemble a silent, music-led 3-world product-tour ad — trim and hard-cut-concat the per-world WIDE-arrival + top-down-macro clips, composite the HTML/Playwright brand end card ("FIND YOUR DAILY." + handwritten scent labels + arrows) over an AI flat-lay background, and mux one music bed into a 720x1280 web master. FREE, deterministic assembly (Playwright + PIL/HTML + FFmpeg); the recipe supplies the config and gates the paid clip/background/music calls. Use for the multiworld-product-tour format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-multiworld
Assemble a silent, music-led "multi-world product tour" ad (≈27s, 9:16) — a tour of three
distinct "third-place" worlds, one per product/scent, that lands on a Pinterest-style brand
end card. Each world is a two-shot pair: a ~4.5s WIDE kinetic-calm ARRIVAL (the
environment dominates, the bottle stays small) hard-cutting to a ~3.5s top-down MACRO
product MOMENT (the sealed bottle nested with its botanical companion). Scent identity is
carried by the world + botanical companion, not by bottle color. No VO, no captions in
the scenes — one music bed carries the whole thing.
This capability is the FREE, deterministic assembler. The paid steps — the six
per-world clips, the AI flat-lay end-card background, the ElevenLabs music bed — are
separate capabilities (see the gap below for the clips); the recipe orchestrates and gates
them.
scene_grid[].duration_sec(arrival 4.5 / macro 3.5), re-encode to the master spec (720×1280, 24fps, yuv420p,
scale+pad, audio stripped). Trimming the macro so the top-down portion dominates also
hides any label misrender at the clip's upright tilt extreme.
end_card.html over the AI flat-layBACKGROUND, then FFmpeg-encode to a dwell_sec (3.0s) static clip. Headline
("FIND YOUR DAILY.", Inter 900), one handwritten Caveat scent label + hand-drawn SVG
arrow per bottle, Playfair wordmark + URL. End-card text is HTML, NEVER AI-rendered —
the AI step produces the background only.
(S01 arrival → S02 macro → … → S06 macro) + the end-card clip. Hard cuts (no dissolves),
normalized to one fps/codec first so concat-copy is safe.
silent concat with afade in/out + loudnorm I=-16:TP=-1.5:LRA=11, AAC 192k, clamped to
27.0s, explicit single-audio map so no silent scene-track leaks in → the H.264 (+ AAC)
720×1280 master.
end-card text. Iterate the cut for free by re-running the assembly.
ONLY on the end card, HTML-composited.
botanical, no hands). Hard cuts between scenes; end card is a static hold with legible
HTML text; music starts at t=0 and fades the tail.
clip; that's a re-roll (paid), not an assembly fix. Identity via world + botanical, never
bottle color.
prompts, end-card copy, music mood); this capability is the generic assembler.
source molecule fires them through Higgsfield Marketing Studio
(marketing_studio_video/product_showcase) grounded on imported product UUIDs, with the
sealed-bottle safety block front-loaded on every prompt. create-video-fal is a FAL i2v
proxy — a different provider and job shape — so it cannot serve this step. Wiring the clip
step to templates-as-data requires a create-video-higgsfield proxy (Marketing Studio
product_showcase, imported-product grounding, per-prompt safety block) that **does not
exist yet**. Until it lands, the clips are generated via the Higgsfield Marketing Studio
path directly (CLI/MCP) and that step is not a fetchable capability.
botanicals) that in prod routes through create-image-fal (NB2); only the HTML text
overlay + encode run locally as FREE assembly.
create-music-elevenlabs; only the loudnorm + fade + mux run locally as FREE assembly.
See scripts/PIPELINE.md for the full config-field → source-step map and scripts/README.md
for the FREE-assembly detail.
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-multiworld 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.