Render Flat Vector Explainer

OrganizationPopular

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-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.

Overview

Publisher	gooseworks-ai
Repository	`goose-skills`
Skill name	`render-flat-vector-explainer`
Stars	1.2K
Forks	208
Bundled files	4
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Render Flat Vector Explainer AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-flat-vector-explainer

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/ads/capabilities/render-flat-vector-explainer .claude/skills/render-flat-vector-explainer

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Flat Vector Explainer in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Render Flat Vector Explainer on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Render Flat Vector Explainer is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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 two non-negotiable separations

Motion layer != text layer. Animate a text-stripped clean plate with Kling i2v (subtle motion, style-preserving negative, cfg 0.5), then composite every chip / numeral / tagline / slate / CTA as an animated Remotion DOM overlay on top. Baking text into the keyframe before i2v warps the type and forfeits the ability to retime/restyle it — this separation is the format's whole credibility.
Real assets != AI assets. The per-step product photo and the closing "N products" grid are real product webps composited with PIL (AI duplicates SKUs in a grid). Only the character vignettes and stylized backgrounds are generative.

Free assembly steps (this capability)

The agent runs these deterministic, $0 steps by hand — see scripts/README.md for the ffmpeg/Remotion/PIL detail:

Remotion overlay — import each Kling clip as the moving base; composite chips / numerals / taglines / slate / grid / CTA as animated DOM on top -> the animated silent master. Slate/grid/CTA beats are Remotion text with no i2v.
PIL product grid — composite the N real product webps on the brand ground for the closing lockup; preserve each aspect (never stretch, never AI-dupe).
Captions — word-by-word burned from the eleven_v3 with-timestamps char timings (libass); suppress on slate/grid/CTA scenes so two text layers don't collide.
Audio mix + master — place each VO line at its scene start, duck the music under VO (sidechaincompress), loudnorm I=-15 VO-forward, mux, burn captions LAST -> finals/master-final.mp4 (~50s).
30s cut — slice each beat's region OUT of the animated silent master (never a static intermediate); trim short beats, gently slow long beats (setpts <=1.6x), re-burn scaled captions -> finals/master-final-30s-v1.mp4.

Paid gen steps (separate capabilities)

The recipe orchestrates and gates these; they are not part of this capability:

Flat-vector character anchor + per-scene keyframes + clean plates -> create-image-fal (nano-banana; re-render a FRESH flat-vector anchor, never chain a photoreal ref).
Kling i2v on the character scenes -> create-video-fal (Kling 2.5-turbo/pro, cfg 0.5, style-preserving negative, low motion; TEST one scene before batching).
Full-sentence VO -> create-vo-elevenlabs (eleven_v3, with-timestamps).
Lo-fi music bed -> create-music-elevenlabs.

Contract

Documentation-grade + FREE assembly (Remotion + PIL + FFmpeg); no paid calls in this capability, no AI-rendered text.
Text is an overlay, never baked. Strip to a clean plate -> i2v -> composite text as Remotion DOM.
Any multi-SKU grid is PIL of the real product webps; preserve each aspect ratio.
Kling holds the 2D flat-vector look only at LOW motion (cfg 0.5 + style-preserving negative). Aggressive motion drifts to photoreal.
Cut down from the ANIMATED master, never a static intermediate; frame-diff to prove localized motion.
The paid steps — keyframes/clean plates, Kling i2v, VO, music — are separate capabilities (create-image-fal, create-video-fal, create-vo-elevenlabs, create-music-elevenlabs); the recipe orchestrates them and gates the spend.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Render Flat Vector Explainer AI skill do?

Why use Render Flat Vector Explainer on TypingMind?

Because you install it once and use it with any model. Render Flat Vector Explainer is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Render Flat Vector Explainer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/ads/capabilities/render-flat-vector-explainer. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Render Flat Vector Explainer?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Render Flat Vector Explainer?

As many as you like. As long as a model supports skills, you can use Render Flat Vector Explainer with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Render Flat Vector Explainer AI skill free?

Yes. It is published on GitHub by gooseworks-ai under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

render-flat-vector-explainer

The two non-negotiable separations

Free assembly steps (this capability)

Paid gen steps (separate capabilities)

Contract

Bundled files

Frequently asked questions

Related AI Skills

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