Render a 'mosaic-grid-reveal' video from a config — a real-DOM FULL-BLEED N×N mosaic of real product tiles that pops in one tile at a time (scatter order, ease-out-back overshoot), the grid clears, then the brand wordmark builds line-by-line followed by a sub-label, tagline, and CTA; frame-stepped via Playwright and encoded with FFmpeg — deterministic assembly, FREE (the music bed comes from create-music-elevenlabs), so the wordmark, tile captions, and CTA stay pixel-crisp. Use for the mosaic-grid-reveal format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-mosaic-grid-reveal
Render the mosaic-grid-reveal format from a config. The signature of this format is a
full-bleed N×N mosaic (default 3×3) of the brand's real product stills that **pops in
one tile at a time** (scatter order, ease-out-back "pop"), building the whole grid; the grid
then clears (scale-up + fade) and a clean end card builds line-by-line — the brand's
real wordmark, then a small sub-label, then the tagline, then a CTA. Silent by design (a
light music bed is muxed separately via create-music-elevenlabs). The hook is RANGE — nine
maximally-distinct variants show how much choice the brand offers.
Everything is a real-DOM HTML scene frame-stepped to PNG via Playwright and encoded with
FFmpeg, so the wordmark, tile captions, and CTA stay pixel-crisp (a video model would
smear them). No generative video, no i2v, no AI-rendered text. This capability is **FREE and
deterministic** — the only paid step of the format (the music bed) is a separate media cap.
config.json)width, height, fps — canvas + frame rate (default 1080×1920, 30).wordmark — brand logo SVG (full <svg> or bare <path> markup) or PNG path. Real DOM,recolored via palette.ink. Never AI-render it. wordmark_viewbox, wordmark_width tune it.
palette — bg (warm off-white), accent + accent_deep (brand color), ink, cap.grid — cols/rows (default 3×3), inset (outer margin), gap.tiles — one per cell (length must equal cols*rows): image (real variant still),name (caption), bg (soft pastel echoing the variant), ink (deep caption color).
pop_order — scatter order across cells (default corners → center → edges).timing — grid_in0, cadence, pop, hold, clear (seconds).end_card — sub, tagline_top, tagline_bottom (bold), cta, url. Brand's own approved copy only.eyebrow — optional top kicker held during the grid.scripts/config.example.json is a filled example (the Pair Eyewear "RANGE" worked example);
the tile image/wordmark paths are brand inputs bound by the orchestrator at remix time.
python3 scripts/build_html.py --config config.json --out hyperframe.html # emits scene, writes duration_sec back
python3 scripts/render.py --config config.json --html hyperframe.html --out master-silent.mp4
build_html.py computes the full timeline and writes duration_sec back into the config so
render.py (which reads dims/fps/duration from the config) frame-steps window.renderAt(t)
at fps and FFmpeg-encodes the silent master. Then mux the create-music-elevenlabs bed:
ffmpeg -i master-silent.mp4 -i bed.wav -map 0:v:0 -map 1:a:0 -c:v copy -c:a aac -shortest out.mp4.
empty margin. Tiles pop in one at a time (scatter order), never all-at-once or static.
prices, or shipping speed.
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-mosaic-grid-reveal 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.