Render a 'brand identity reveal' video from a config — a single poster frame in a real, softly-lit space (real wall, soft-focus plant in the corner, dappled leaf shadow, illuminated poster) whose artwork HARD-CUTS through ~10 on-brand poster mockups (hero product, IG post, hanging banners, sticker sheet, logo lockup, poster, two lifestyle stills, packaging, big icon) then holds on a brand end card. The environment plate is one create-image-fal generation; the mockups are real-DOM HTML frame-stepped via Playwright, perspective-composited into the detected frame quad with the plate's real leaf-shadow multiplied back onto each poster (reads as behind glass), sequenced by FFmpeg. Deterministic assembly, FREE (the plate comes from create-image-fal, the bed from create-music-elevenlabs), music bed only and approved brand copy only. Use for the brand-identity-reveal format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-brand-identity-reveal
Render the 'brand identity reveal' format from a config. The signature is a single fixed
poster frame in a REAL, softly-lit space (the illuminated poster-frame look of a
boutique/cinema): a real wall, a real soft-focus plant in the lower-left corner, dappled
leaf shadow. The artwork INSIDE the frame HARD-CUTS (no crossfade) through 11 beats — 10
on-brand poster mockups + a brand END CARD held ~3s. Music bed only, NO voiceover; copy is
baked into each mockup, never overlaid as captions.
This capability is the FREE assembly only. The paid parts are separate generic
capabilities the recipe names — create-image-fal (the one-shot environment plate) and
create-music-elevenlabs (the bed). Never re-implement them here.
create-image-fal, flux-pro ultra, 9:16) — an *empty* litposter frame in a real space, high-res so the camera can be pushed closer by cropping.
Pick a wall color that makes the brand's poster colors POP (complement of the dominant hue).
scene.html), one poster per beat, built from thebrand's real assets + approved copy, frame-stepped via Playwright (render_art.py, bare
mode, device_scale_factor 2).
measure_frame.py detects the blank poster interior quad;composite.py perspective-warps each poster into it AND multiplies the plate's real
leaf-shadow/light back onto the poster (so it reads as behind glass) + a glass sheen;
build_video.sh sequences the frames with the music bed.
config.json — copy scripts/config.example.json and edit (canvas, plate prompt +wall_style, camera crop, per-beat durations, end-card copy). Schema + per-file working-dir
layout are documented in scripts/PIPELINE.md.
as an SVG <mask> if the only source has an occluding element). Approved copy only.
CAP=skills/ads/capabilities/render-brand-identity-reveal
RUN=<project>/working # holds scene.html + assets/ + the create-image-fal plate in bg/
python3 $CAP/scripts/recrop.py 0.72 0.13 # camera distance (bigger frac = bigger frame)
python3 $CAP/scripts/measure_frame.py # detect the blank poster interior quad
python3 $CAP/scripts/render_art.py # render each poster standalone (Playwright, dsf 2)
python3 $CAP/scripts/composite.py # warp into frame + shadow multiply + sheen
bash $CAP/scripts/build_video.sh $RUN/concat.txt $RUN/music.mp3 out.mp4 13.05 1.0
Then watch the master (music-only, every poster legible, shadow falls across the art, end
card holds). See scripts/PIPELINE.md for adapting scene.html per brand.
display (a per-state display:flex silently overrides adisplay:none toggle and paints one state over all others).
recrop.py), don't regenerate.display: rule beat the visibility toggle; useopacity.
evaluate(fn, arg) didn't switch state in a loop → bake the beat index into theJS string and return the applied state to assert it.
corner-flood-fill.
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-brand-identity-reveal 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.