mcpbeat Sign in

Render Mosaic Grid Reveal Agent Skill

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.

5k tokens
context cost
the whole folder, loaded on every use
7
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
1086
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-mosaic-grid-reveal

What comes with it

16 839 bytes besides the instruction
scripts/_shared.js
scripts/build_html.py
scripts/config.example.json
scripts/render.py
skill.meta.json
tests/smoke-test.md

The instruction itself

4 sections, as written by the author

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.

Inputs (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.

  • palettebg (warm off-white), accent + accent_deep (brand color), ink, cap.
  • gridcols/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).
  • timinggrid_in0, cadence, pop, hold, clear (seconds).
  • end_cardsub, 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.

Run

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.

Rules

  • FILL the frame — tiles are full-bleed and edge-to-edge, NOT small cards floating in an

empty margin. Tiles pop in one at a time (scatter order), never all-at-once or static.

  • Real wordmark + real product stills only. Never AI-render the logo or text.
  • Copy slots are the brand's own approved lines — never invent claims, customers, results,

prices, or shipping speed.

  • Pick the 9 most DISTINCT variants (color-wheel spread) — that IS the RANGE hook.

Other skills for the same job

different authors, same section of the catalogue
Canvas Design
by anthropics
vendor ×13

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.

1388k tokens
Algorithmic Art
by anthropics
vendor ×10

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.

15k tokens scripts
Image Enhancer
by frostant
×6

Improves the quality of images, especially screenshots, by enhancing resolution, sharpness, and clarity. Perfect for preparing images for presentations, documentation, or social media posts.

635 tokens
Video Downloader
by CommandCodeAI
×4

Downloads videos from YouTube and other platforms for offline viewing, editing, or archival. Handles various formats and quality options.

671 tokens
Histolab
by christophacham
×3

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.

18k tokens
Omero Integration
by christophacham
×3

Microscopy data management platform. Access images via Python, retrieve datasets, analyze pixels, manage ROIs/annotations, batch processing, for high-content screening and microscopy workflows.

32k tokens
Pydicom
by christophacham
×3

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.

13k tokens scripts
Transformers
by christophacham
×3

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.

13k tokens

How to use it

Copy the folder

Take gooseworks-ai/render-mosaic-grid-reveal from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

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.