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Get Started Free →gooseworks-ai/render-flat-vector-explainer
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-
Passed 21 of 23 test cases with the skill, and 11 of 23 without it.
and 11 more in the full report
10 fixed11 passed either way2 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
This benchmark measured an earlier version of the skill.
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
For our 4-product beverage routine video, we need an end card showing all 4 cans lined up on the brand background. Should we prompt an image AI like Midjourney or Fal to render the 4-can product grid layout directly, or assemble it programmatically?
| Checked | Without skill | With skill |
|---|---|---|
| Rejects using generative AI models to render the multi-product grid. | Passed | Passed |
| Prescribes compositing the real product webp assets using Python Imaging Library (PIL). | Missed | Passed |
The instructions your model reads when the skill loads.
Assembles a flat-vector product-routine explainer: one illustrated host character walks through a countable N-step routine (e.g. the demo's 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.
Creative calls the user makes (the recipe's choices, asked in one round before any paid step). The demo's value is only an example, never the default. The flat-vector 2D style itself is the format, not a choice.
host — who the illustrated host is (gender, age, look, outfit) -> character.anchor_prompt. Asked; the demo used a late-20s woman with brown wavy hair in a cream slip-dress.setting — where the routine happens -> the character keyframe prompts. Asked; the demo used a bathroom counter.tone — the script's tone -> VO lines + expressions. Asked; the demo used faintly comedic overwhelm -> satisfied payoff.narrator_voice — the VO voice -> voice.voice_id. Asked; the demo used ElevenLabs "Eryn".music — the bed -> music.prompt (or none). Asked; the demo used lo-fi pop, 95-105 BPM.scripts/config.example.json is the Spoiled Child worked example: copy its structure, never its creative values or its brand palette.
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).test cases passed, vs 11 / 23 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.