Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video — birefnet cutout, then a cold-open text card + product carousel + annotated specimen-sheet end card, plus loudnorm + separate-pass music mux. FREE assembly (PIL + rsvg + FFmpeg); the recipe supplies the config and gates the paid BG/cutout/music calls to their own capabilities. Use for the vignette format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-vignette
Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video. Motion lives in the BG video; the product rides on top as a static cutout layer. Music-led, zero VO, sub-12s, loopable — it reads muted because the product label + on-screen copy carry the message. Defaults to the V-CARD structure: a cold-open text card → a product carousel under one shared BG → an annotated specimen-sheet end card.
strip_product_backgrounds.py — birefnet cutout of each PDP shot to clean hard-edge alpha (no halo/shadow).render_overlays.py — PIL + rsvg-convert render the cold-open card (Boska Black, dead-center) + the annotated specimen-sheet end card (brand SVG logo + Space Grotesk annotations) as transparent 1080x1920 PNGs. FREE.composite_variants.py — one FFmpeg filter_complex per BG variant: BG (palette-aware dim) → cold-open overlay → cutouts (width-anchored, vertically centered y=(H-h)/2) → end card. h264 crf20 yuv420p +faststart 30fps. FREE.music_and_mux.py — instrumental music bed → acompressor + loudnorm I=-18:TP=-2:LRA=9 → muxed into every variant in a SEPARATE pass with explicit -map 0:v:0 -map 1:a:0. The mux is FREE; the music generation is a paid call that in prod routes through create-music-elevenlabs.y=(H-h)/2) so mixed-shape SKUs share one visual mid-line — never bottom-anchor (squat jars jump). For 9:16 scale by WIDTH (~75% tall bottles, ~65% squat jars).saturation=0.50); naturally-contrasty BG → lighter dim (saturation=0.85).-map 0:v:0 -map 1:a:0 (single-pass composite+mux silently ships 1 kbps garbage audio).strip_product_backgrounds.py calls fal-ai/birefnet/v2 directly via a fal_helpers shim (sys.path.insert into a shared atoms dir + from fal_helpers import ...). It is bundled here because the cutout is an intrinsic assembly step, but in prod the birefnet cutout should route through create-image-fal (the fal-proxy capability that bills the Ads agent) rather than hitting fal.run directly. Treat the direct-fal path as a gap to close; the recipe gates the cutout to gooseworks fetch create-image-fal.source/t2v-outputs/<slug>.mp4 so the composite is source-agnostic. There is no Pexels fetcher bundled here; that lives in the recipe's playbook.music_and_mux.py also uses the fal_helpers shim for the ElevenLabs music generation; in prod that generation routes through create-music-elevenlabs, and only the loudnorm + separate-pass mux run locally as FREE assembly.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-vignette 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.