Render an 'Instagram-Live social-proof gallery' video from a config — ~5 real brand product stills each framed as an Instagram-LIVE card (IG gradient-ring avatar, username, verified check, red LIVE badge, viewer count, close X, a short claim-free live-comment feed, an empty 'Add a comment…' bar, and reaction hearts floating up the right edge), with a brand-approved benefit sentence building one phrase per slide and a clean logo endcard, rendered deterministically with PIL frames plus FFmpeg — FREE (the music bed comes from create-music-elevenlabs), products and wordmark stay crisp. Use for the ig-live-gallery format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-ig-live-gallery
Render the ig-live-gallery format from a config. Each of the brand's REAL hero product
stills is presented as the "live video" inside an authentic Instagram-Live card — dark
gradient scrims top & bottom over the bright product, an IG gradient-ring avatar + username +
verified check + red LIVE badge + viewer count + close X across the top, a short live-comment
feed + an EMPTY "Add a comment…" bar + heart/share icons across the bottom, and pink/red
reaction hearts floating up the right edge. A benefit sentence builds one short phrase per
slide as the top overlay, and it closes on a clean brand logo card with the payoff line + CTA.
Deterministic and FREE — no generative image/video, no AI-rendered text. The paid music bed is
a separate capability (create-music-elevenlabs) muxed on afterward.
python3 scripts/build.py --config config.json --assets <stills-dir> --out master-silent.mp4
# quick check — one frame per slide:
python3 scripts/build.py --config config.json --assets <stills-dir> --stills <dir>
config.example.json is a full worked config (Glossier). Config shape:
username, verified, palette, slide_dur, endcard_dur, crossfadeslides[] — { image, phrase, viewers, comments: [[handle, text], …] } per productendcard — { logo, logo_is_white, payoff, handle, cta }music — { prompt, length_ms, trim_intro_sec } (consumed by create-music-elevenlabs)Image paths in the config resolve against --assets.
drop-shadow otherwise inflates the box and the product reads shifted sideways. The renderer
measures the top 50% of the silhouette with an L<246 threshold (catches white-product edge
shading too) and centers that midpoint.
product — that's fine; don't shrink them to a thin edge ribbon.
PIL can't render color emoji); the comment INPUT bar stays empty. LIVE badge + viewer count +
hearts are ambient live-stream chrome, not proof claims.
A silent WxH (default 1080x1920) H.264 master. Generate + mux the create-music-elevenlabs
bed with a short fade in/out + loudnorm (-map 0:v:0 -map 1:a:0), then QC with watch.
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-ig-live-gallery 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.