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Render Split Screen Creator

OrganizationPopular
gooseworks-ai
render-split-screen-creator

Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local Whisper (not the raw VO — concat drops inter-scene silence) and word-level captions burned in the chosen style. This is the FREE deterministic assembly + caption stage (two-zone composite + blurred fill + divider + hard-concat + end card + captions); the VO comes from create-vo-elevenlabs, the anchor from create-image-gpt-image-fal, and the whole-VO lip-sync from a paid VEED Fabric 1.0 take (a no-atom upstream input). Use for the split-screen-creator format.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namerender-split-screen-creator
Stars
1.2K
Forks
208
Bundled files
4
LicenseMIT
Links
  • 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 Split Screen Creator AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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-split-screen-creator .claude/skills/render-split-screen-creator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Split Screen Creator 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 Split Screen Creator 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 Split Screen Creator 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-split-screen-creator

Assemble a split-screen creator ad from a config: a two-zone vertical (1080×1920, 9:16, ~40s) format where an AI creator talking-head anchors the BOTTOM ~48% of the frame and real 16:9 product/demo clips run uncropped in the TOP ~52%, a 3px brand-color divider between the zones. The creator delivers the whole VO cold-to-camera and each top clip proves the claim its VO line makes. This capability is the FREE, deterministic assembly + captions — the two-zone composite (contain-fit + blurred-cover fill + divider + creator slice), the hard-concat, the end card, and the word-level caption burn from the assembled cut.

scripts/config.example.json is the worked example (Perplexity concept-10 "Bloomberg terminal", ~40s 1080×1920 9:16, 6 scenes + an end card); scripts/PIPELINE.md maps every config block to its source step and scripts/README.md documents the free assembly.

Run

This is the FREE, deterministic assembly + captions stage — it spends nothing. The paid inputs are separate steps — the VO (create-vo-elevenlabs, ElevenLabs eleven_v3 with-timestamps, sliced into per-scene windows), the photoreal MEDIUM chest-up AI-creator anchor (create-image-gpt-image-fal, gpt-image-2) — shot at a natural webcam distance (headroom + shoulders, real room), not a plain-background close-up headshot (see the anchor note below), and the whole-VO lip-sync (a paid VEED Fabric 1.0 @ 720p take — a no-atom step, image_url = the anchor, audio_url = the vo mp3, ~$0.15/sec, ~$5.90 for a 39s VO; run its calls sequentially, veed/fabric-1.0 storage-auths 403 under parallel load). Given the creator lip-sync take + the per-scene VO timing + one 16:9 top clip per scene + the scene-1 hook graphic + the end-card clip, render-split-screen-creator composites the two zones, hard-concats the scenes, appends the end card, transcribes the assembled cut, and burns the captions → the master. Re-cuts reuse the existing VO / lip-sync / clips and cost $0.

Contract (the free assembly)

  • Two-zone split, top ~52% / creator ~48%. The TOP zone runs the real 16:9 product/demo clip contain-fit (uncropped), the BOTTOM zone is the creator lip-sync framed head-to-shoulders: scale-to-width × a small ZOOM (~1.15–1.2) then crop the zone with a downward offset so the face sits upper-middle and the shoulders enter the bottom. (A plain cover + crop-toward-top shows only the head and cuts the shoulders — and it can't rescue an anchor that was shot too close; fix the anchor distance first.) Tune zoom/offset visually against the source video's creator framing — it's a FREE re-assemble, no VEED re-run. A 3px brand-color divider separates the zones. Canvas 1080×1920, top_height ~998. Keep every stacked height EVEN (998 + 4 divider + 918 = 1920) — libx264 rejects odd dimensions.
  • Anchor = photoreal MEDIUM shot, not a studio headshot. The lip-sync only looks as good as the anchor. It must be photoreal/candid (real lived-in room, natural skin cues), framed chest-up at a natural webcam distance (headroom + shoulders), not a plain-background close-up and not a phone-selfie pose copied from another format (e.g. ugc-walk-and-talk). VEED Fabric handles photoreal fine (unlike Seedance).
  • Blurred-cover fill, never black bars. The top clip's letterbox margins are filled with a darkened blurred cover-scale of the same clip — a flat charcoal/black bar reads cheap.
  • One claim per scene, shown as it's said. Each top clip is windowed (top_start/top_end) to the on-message segment that proves its VO line. Never loop a short clip — set the window and the assembler speed-fits it to the scene (looping replays into a sparse/black tail).
  • The creator VO is the entire audio bed — no separate music. Body audio = the concatenated creator VO slices, timed per the per-scene timing.json; the lip-sync drives the mouth.
  • Hard-concat the scenes; end card on the last SHARP frame. Hard-cut concat (no dissolves); append the end card holding the last sharp frame ~3s. If the end-card clip fades to black, hold the last sharp second (endcard.clip_end), not the black tail.
  • Caption the ASSEMBLED cut, not the raw VO. Concat drops inter-scene silence, so the ad timeline ≠ the VO timeline; only the final cut's audio yields correct caption timing. Transcribe the assembled cut with local Whisper, build word-level cues (sentence-aware chunking), burn the ASS in the chosen style (serif-accent, kinetic-pop, …). Keep the -precaption cut + the .ass sidecar so captions restyle without re-rendering the composite. If the host ffmpeg lacks libass, render the cues as timed PIL PNG overlays (ffmpeg overlay=…:enable='between(t,st,en)') at the same placement.
  • FFmpeg composite, deterministic, FREE. Two-zone composite per scene, hard-concat, append the end card, mux the creator VO, loudnorm I=-14 → a 1080×1920 h264+aac master. No paid calls, no keys — the VEED Fabric lip-sync is a supplied input, produced upstream.

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 Split Screen Creator AI skill do?

Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local W...

Why use Render Split Screen Creator on TypingMind?

Because you install it once and use it with any model. Render Split Screen Creator 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 Split Screen Creator 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-split-screen-creator. 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 Split Screen Creator?

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 Split Screen Creator?

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

Is the Render Split Screen Creator 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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