Assemble the FREE steps of the flat-vector-explainer video format — a flat-illustration creator-character walks a countable N-step product routine, one step per beat, and Remotion composites every chip/numeral/tagline/slate/CTA as an animated DOM overlay ON TOP of the Kling i2v character clips (text is NEVER baked into a keyframe — i2v warps type), the closing 'N products' grid is a PIL composite of the REAL product photos (not AI), full-sentence VO drives word-by-word burned captions over a VO-forward music bed, and the ~50s animated silent master is re-cut to a 30s deliverable FROM the animated master (never a static intermediate). Documentation-grade — ships config.example.json + PIPELINE.md + a README of the free assembly; the paid gen steps (keyframes, Kling i2v, VO, music) are separate capabilities the recipe orchestrates. Use for the flat-vector-explainer format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-flat-vector-explainer
Assembles a flat-vector product-routine explainer: one illustrated creator-character walks through a countable N-step routine (e.g. collagen -> serum -> eye cream -> hair), one step per beat, each beat carrying a large corner numeral, a labelled chip + one-line tagline, and the step's real product photo, closing on an "N products" grid + brand CTA. It reads as a premium DTC explainer (Spotify/Anchor flat-vector lineage), not UGC.
This capability is documentation-grade. The content-goose molecule is a documented recipe, not a runnable end-to-end app, so this capability ships the config schema (scripts/config.example.json), the field-to-script map (scripts/PIPELINE.md), and a README (scripts/README.md) describing the FREE assembly steps the agent runs by hand with ffmpeg + Remotion + PIL. The paid generative steps are separate capabilities the recipe orchestrates and gates.
The agent runs these deterministic, $0 steps by hand — see scripts/README.md for the ffmpeg/Remotion/PIL detail:
loudnorm I=-15 VO-forward, mux, burn captions LAST -> finals/master-final.mp4 (~50s).finals/master-final-30s-v1.mp4.The recipe orchestrates and gates these; they are not part of this capability:
create-image-fal (nano-banana; re-render a FRESH flat-vector anchor, never chain a photoreal ref).create-video-fal (Kling 2.5-turbo/pro, cfg 0.5, style-preserving negative, low motion; TEST one scene before batching).create-vo-elevenlabs (eleven_v3, with-timestamps).create-music-elevenlabs.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-flat-vector-explainer 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.