Assemble the FREE steps of the product-hypermotion + kinetic-typography video format — dice ONE Seedance 2.0 hypermotion clip into 5-6 segments and intercut them with PIL kinetic-typography spec/CTA cards (italic skew, 1.08x outline echo, 3D extrusion, slam-with-shake, inversion flash) plus a real-logo PIL end card (base64-decoded from the brand SVG), center-crop 1:1 to 9:16, and explicit-map mux a music bed. Deterministic, FREE (PIL + FFmpeg), no paid calls, the real logo and spec typography stay pixel-crisp. The paid steps (the ONE Seedance i2v, the music) are separate capabilities; the recipe orchestrates them. Use for the product-hypermotion format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-hypermotion
Assembles a product-hypermotion + kinetic-typography ad: a vertical 9:16 sizzle (~20–30s)
where ONE spectacular AI-gen hypermotion clip of the hero product carries the energy and
punchy PIL-rendered spec cards carry the facts. Music-led, no VO — it reads as a
high-production sizzle, not UGC.
The reusable IP is one-call-many-cuts + intercut: dice ONE 12–15s Seedance 2.0
hypermotion i2v into 5–6 segments (never a paid call per segment) and interleave PIL
kinetic-typography spec cards between the cuts, capped by a real-logo end card, over a
124 BPM bass bed.
This capability ships the FREE, deterministic assembly — everything between the two paid
model calls. It is documentation-grade + config: the format has no runnable driver.
scripts/ carries the worked config + the step-by-step pipeline doc; the Soundboks
reference PIL impls (gen_kinetic_v6.py, gen_endcard_v10.py, assemble_v10.py) are
copied + adapted per run, not vendored.
the 5-block Seedance prompt, per-card treatments, beat structure, end-card spec, music
brief, dims. Copy to config.json and edit.
Seedance + music (parallel, gated) → Phase 2 FREE PIL cards → Phase 3 FREE
dice/intercut/concat/mux → Phase 4 watch/QC. Names the atom/tool each step uses, plus
20s/25s/30s beat-structure variants.
frame-by-frame on a dark grain BG with the static Space Grotesk Bold TTF: italic skew
(~6°), outline echo at 1.08× (bleed-safe), 3D extrusion (hero stat + CTA, side = brand
accent), slam-with-shake, and color/inversion flash. No VO — the cards carry every fact.
SVG, never typeset) with a slam-motion-blur entry, settle, continuous micro-motion (±1%
scale + ±3px drift for the full hold — never freeze), an inversion flash at ~60%, and a
cascade reveal of the spec-dot subtitle + CTA.
5–6 decreasing-length segments at Seedance's natural beats (crash-zoom → orbit → settle),
then concat in the fixed intercut order: open on the intro card, alternate segment ↔ spec
card, end on the CTA + brand end card.
-map 0:v -map 1:a(the default mapping silently ships ~1 kbps garbage audio; verify ffprobe ≈ 192 kbps).
# Phase 2 — FREE PIL cards (per config.text_cards + config.end_card)
# render each spec/CTA card + the real-logo end card, 1080x1920, frame-by-frame → mov
# Phase 3 — FREE dice + intercut + mux
# center-crop 1:1→9:16, dice into 5-6 segments, concat in beat_structure order,
# then explicit-map mux the music bed → master-final.mp4
Output: master-final.mp4, 1080×1920, ≈20–30s (25s default) h264 (+ 192 kbps aac music) —
14 segments: intro + 6 hypermotion cuts + 5 spec cards + CTA + real-logo end card. All
FREE, $0.
AND identity-consistent: same camera/grade/subject in one call).
typeset; it must micro-move for the full hold (never freeze).
frame; use the static Space Grotesk Bold TTF (variable renders as Regular in PIL).
ABSOLUTE CONSTRAINTS block, or the geometry drifts) + the ElevenLabs 124 BPM bass bed —
are separate capabilities (create-video-fal, create-music-elevenlabs); the recipe
orchestrates them and gates the spend.
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-hypermotion 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.