Render a punchy ~12s vertical (9:16) music-only direct-response OFFER ad as a 4-beat kinetic-typography film — HEADLINE slam → real PRODUCT drop → CLAIM/proof → CTA pill — from one config of copy slots, a real product photo, a brand palette, fonts, bpm, and beat split. DETERMINISTIC + FREE (a bundled Remotion project; springs + interpolate, no AI-gen for visuals). Backgrounds are engine gradient divs off the palette, props are inline SVG, the ONLY composited bitmap is the REAL product photo (objectFit:contain, never stretched), and ALL headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered (the format's credibility guard). A driver binds the config to Remotion input props, renders the 9:16 master, and derives a 1:1 center-crop with ffmpeg. Two gating checks run before render (claim verbs must match the product's physical format; the claim beat needs an edge-entry mechanism prop). Use for the motion-graphics-offer-ad format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-offer-ad
The free, deterministic renderer for the motion-graphics-offer-ad format — the
punchy ~12s vertical, music-only, direct-response offer ad built as a **4-beat kinetic
-typography film**: a HEADLINE slams in word-by-word → the real PRODUCT drops in →
the CLAIM/proof lands → a CTA pill resolves. No character, no VO, no captions — the
on-screen typeset text IS the message.
This is a bundled Remotion project (project/) driven by a thin Python driver. The
shipping master is 100% engine-rendered (springs + interpolate): backgrounds are
gradient divs off the brand_palette, props are inline SVG, and the ONLY composited
bitmap is the REAL product photo (objectFit:contain, never stretched). ALL
headline/claim/CTA/URL/wordmark text is typeset in the engine — never AI-rendered;
that is the format's credibility guard. Render cost is ~$0; the only paid step is an
optional music bed, gated upstream in the recipe to create-music-elevenlabs.
The whole ad is data: copy strings, product photo, palette, fonts, bpm, and beat
split all arrive as config.json and are bound to Remotion input props — nothing is
hardcoded in the scenes (the source run's Spoiled Child strings are generalised into
project/src/props.ts). Deterministic → iterate the cut for free.
headline_words slam in WORD-BY-WORD(slamIn, ~7-frame stagger, scale-overshoot + motion-blur smear, settling on the
downbeat); subline + an animated bobbing down-arrow drop in.
dropIn) andidle-bobs (bob), objectFit:contain (never stretch); the motif_chip pops in
(popIn); optional GENERIC competitor shape + strike-through (wipe) — never a
named competitor.
mechanism_prop slides in from a frame edge(flyIn overshoot, ~20% from the bottom) to add motion AND show the mechanism; the
3-line claim drops in staggered; product held bottom-right.
wordmark slams (slamIn); the CTA pillpops in as the motif-chip handoff resolving (popIn) with an arrow nudge; the
cta_url fades up.
Scene boundaries are derived from beat_split_sec (default [3.0, 3.5, 3.0, 2.5]s →
cuts at frames 0/90/195/285/360 @30fps) so the hard cuts land on the beat downbeats. The
motion kit — slamIn / dropIn / bob / flyIn / popIn / wipe — lives in
project/src/lib/anim.ts and is kept intact from the source run.
format: liquid → spoon / sip / drink / 0 mess; powder → scoop / mix / no clumps;
capsule → 1 a day; gummy → chew. render.py rejects foreign-format vocabulary
(e.g. a liquid product must not borrow powder grammar like scoop / no clumps). Set
product_format in the config to arm this gate.
mechanism_prop (spoon for drinkable liquids, or accent for a neutral edge-entry
accent bar) that supplies motion AND shows the mechanism. A text-only claim beat is too
static — render.py rejects an unsupported/missing prop.
# 1) install the bundled Remotion project's deps (one-time; render.py auto-runs
# this if node_modules is absent).
cd project && npm install && cd ..
# 2) bind config → Remotion input props, render the 9:16 master, derive the 1:1 crop.
python3 scripts/render.py \
--config path/to/config.json \
--work-dir <work> \
--out <work>/master
# → <work>/master-9x16.mp4 (1080x1920) and <work>/master-1x1.mp4 (1080x1080 crop)
render.py copies the config's product_hero_image (+ optional music.bed) into
project/public/, maps every config key onto the OfferAdProps shape props.ts reads,
writes the input props to <work>/props.json, runs `npx remotion render offer-ad --props
<work>/props.json`, then center-crops the 9:16 master to 1:1 with ffmpeg (no 2nd
composition). NO hardcoded /Users or clients paths — every asset arrives via the
config and a runtime --work-dir.
scripts/render.py — the driver: gating checks → stage assets into project/public/→ bind config to Remotion input props → npx remotion render (9:16) → ffmpeg 1:1
center-crop.
scripts/config.example.json — the shape of the config the recipe binds(brand-neutral worked defaults from the source run; replace every /abs/... placeholder).
project/ — the bundled Remotion project. src/props.ts (the input-props schema +beat-layout math + safe defaults), src/lib/anim.ts (the `slamIn/dropIn/bob/flyIn/
popIn/wipe motion kit — kept intact), src/scenes/Scene01–04.tsx` (the four beats,
all copy/palette/fonts from props), src/Main.tsx (the beat spine + audio bed),
src/Root.tsx (the offer-ad composition; duration derived from beat_split_sec).
| config key | Remotion prop (props.ts) | scene that reads it |
|---|---|---|
| product_hero_image | product_image (staged to public/) | Scene02 (drop), Scene03 (anchor) |
| brand_palette{primary_ground,light_ground,ink,highlight_chip} (dict OR ≥4-hex list) | palette | all 4 (grounds/type/chip) |
| fonts{display,body} | fonts (resolved in fonts.ts) | all 4 |
| copy.headline_words[] / copy.subline | copy.headline_words / copy.subline | Scene01 |
| copy.motif_chip | copy.motif_chip | Scene02 |
| copy.claim_lines[]{big,small} | copy.claim_lines | Scene03 |
| copy.cta_label / copy.cta_url / copy.wordmark | same | Scene04 |
| mechanism_prop (spoon\|accent) | mechanism_prop | Scene03 (inline-SVG prop) |
| bpm | bpm | beat grid |
| beat_split_sec [4] | beat_split_sec | derives cut frames + total duration |
| music.bed / music.fade_out_frames | music.src (staged) / music.fade_out_frames | Main audio bed |
| show_competitor_strike | show_competitor_strike | Scene02 |
| aspects | (driver) | 9:16 always; 1:1 = ffmpeg crop |
transparent bg; objectFit:contain, never stretched.
engine. AI never draws a letter in this format.
static/zoompan cut was rejected as "too static" on the source run.
downbeats; the bed volume ramps to 0 over the last music.fade_out_frames inside the
render. No VO, no captions.
silhouette (show_competitor_strike), not a named brand.
grammar; the claim beat must have real prop motion, not a static text stack.
@remotion/cli ≥4.0, installed via the bundledproject/package.json — render.py runs npm install in project/ on first use).
This capability legitimately needs a node runtime — the renderer IS a Remotion project.
watch (QC the final master — confirm the headline slams WORD-BY-WORD on the beat, thereal product photo drops in + idle-bobs and is NOT stretched, the mechanism prop enters
from a frame edge on the claim beat, the claim lines drop in staggered, the CTA label +
URL are readable at 1080×1920, the hard cuts land on the downbeats, and the music ramps
out; re-confirm both gating checks visually). The recipe gates the only optional paid
step (music bed → create-music-elevenlabs) — this capability itself makes NO paid calls.
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-offer-ad 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.
The instructions reference npm, npx.
Without those the skill loads but fails at the first command.