Build the deterministic PIL pill overlays for a 'perfect-score + proof-points' UGC video ad (white 10/10 score header with a medal + orange sub with a finger-down + 3-4 green-check proof pills) and composite them onto a base clip in a diagonal L->R->L->R cascade, then mux the music bed into master-final.mp4. Config-driven (config.json), 1080x1920 9:16, FREE and deterministic (no paid calls, text stays pixel-crisp). Use for the overlay-proof-points format; the base clip + music come from separate paid capabilities.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-proof-points-overlay
Build the deterministic PIL/FFmpeg overlays for the "Instagram comparison-tool reviewer" UGC ad — a white "we got a perfect 10/10 score" headline pill (trailing medal), an orange "but here's also why you'll love us" sub pill (trailing finger-down, width-matched to the header), and 3-4 green-check proof pills — then composite them onto a base clip in the format's signature diagonal cascade and mux the music into the master. FREE and deterministic: the pills are PIL-rendered so the score, checks, and wordmark stay pixel-crisp (a video model would smear type). The base clip (create-image-fal keyframe -> create-video-fal i2v) and the music bed (create-music-elevenlabs) come from separate paid capabilities; this one only does the free rendering.
fetch_icons.py --run-dir <run> ; build_overlays.py --config config.json --out-dir <run>/generated/overlays ; compose_master.py --config config.json --run-dir <run> — reads <run>/generated/clip-handheld.mp4 + generated/music-bed.m4a, writes <run>/master-final.mp4. 1080x1920, deterministic, $0. (Add --no-music to compose for a silent design preview.)
fetch_icons.py — downloads the three Twemoji PNGs (medal 1f3c5, finger-down 1f447, check 2705) to <run>/assets/icons. PIL cannot render Apple Color Emoji, so pills paste Twemoji PNGs. Free, local.build_overlays.py — PIL: renders the white score header (trailing medal), the orange subhead (trailing finger-down, width-matched to the header), and N green-check proof pills auto-sized to their copy. Bold weight and icon-centered-on-pill-middle are load-bearing.compose_master.py — FFmpeg: scale/crop the base clip to 1080x1920@30, composite the always-on headers, cascade the proof pills (each enable='gte(t,T)' on its own alternating LEFT/RIGHT row), mux the music, apply the anti-AI grain pass, re-encode crf23/maxrate12M -> master-final.mp4.config.json (overlays, layout, duration_sec, optional music/post_production); the template recipe supplies the config from recipe.config.build_overlays.py before compose_master.py — the compositor reads pre-rendered PNGs and silently reuses stale ones on a copy change.Pillow + ffmpeg. No API keys.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-proof-points-overlay 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.