Render Offer Ad logo

Render Offer Ad

OrganizationPopular
gooseworks-ai
render-offer-ad

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.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namerender-offer-ad
Stars
1.2K
Forks
208
Bundled files
17
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.

  • 17 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 Offer Ad 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-offer-ad .claude/skills/render-offer-ad
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Offer Ad 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 Offer Ad 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 Offer Ad 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-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.

The 4 beats (the spine)

  1. HEADLINE — primary-color radial ground; 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.
  2. PRODUCT — light radial ground; the REAL product photo drops in (dropIn) and idle-bobs (bob), objectFit:contain (never stretch); the motif_chip pops in (popIn); optional GENERIC competitor shape + strike-through (wipe) — never a named competitor.
  3. CLAIM — light radial ground; the 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.
  4. CTA — primary-color radial ground; the wordmark slams (slamIn); the CTA pill pops 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.

Two gating checks (run before any render — from the recipe)

  • (a) CLAIM-MATCHES-FORMAT — the claim/proof verbs MUST match the product's physical 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.
  • (b) CLAIM-BEAT MECHANISM PROP — the claim beat must include an edge-entry 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.

Run

bash
# 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

  • 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 → props mapping (what binds to what)

config keyRemotion prop (props.ts)scene that reads it
product_hero_imageproduct_image (staged to public/)Scene02 (drop), Scene03 (anchor)
brand_palette{primary_ground,light_ground,ink,highlight_chip} (dict OR ≥4-hex list)paletteall 4 (grounds/type/chip)
fonts{display,body}fonts (resolved in fonts.ts)all 4
copy.headline_words[] / copy.sublinecopy.headline_words / copy.sublineScene01
copy.motif_chipcopy.motif_chipScene02
copy.claim_lines[]{big,small}copy.claim_linesScene03
copy.cta_label / copy.cta_url / copy.wordmarksameScene04
mechanism_prop (spoon|accent)mechanism_propScene03 (inline-SVG prop)
bpmbpmbeat grid
beat_split_sec [4]beat_split_secderives cut frames + total duration
music.bed / music.fade_out_framesmusic.src (staged) / music.fade_out_framesMain audio bed
show_competitor_strikeshow_competitor_strikeScene02
aspects(driver)9:16 always; 1:1 = ffmpeg crop

Craft rules (load-bearing — faithful to the source run)

  • The composited bitmap is the REAL product photo — never an AI packshot. Clean / transparent bg; objectFit:contain, never stretched.
  • Never AI-render text. Headline, claim, CTA, URL, wordmark — all typeset in the engine. AI never draws a letter in this format.
  • Never Ken-Burns/zoompan over stills — the engine springs carry the motion; a static/zoompan cut was rejected as "too static" on the source run.
  • Music-only, beat-locked — the cuts + big slams land on the 0.0/3.0/6.5/9.5s downbeats; the bed volume ramps to 0 over the last music.fade_out_frames inside the render. No VO, no captions.
  • Never name/jab a competitor — any comparison shape on the product beat is a GENERIC silhouette (show_competitor_strike), not a named brand.
  • The two gating checks are non-optional — a liquid product must not borrow powder grammar; the claim beat must have real prop motion, not a static text stack.

Requires

  • Node.js (≥18) + Remotion (@remotion/cli ≥4.0, installed via the bundled project/package.jsonrender.py runs npm install in project/ on first use). This capability legitimately needs a node runtime — the renderer IS a Remotion project.
  • ffmpeg on PATH (for the 1:1 center-crop).
  • watch (QC the final master — confirm the headline slams WORD-BY-WORD on the beat, the real 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.

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 Offer Ad AI skill do?

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...

Why use Render Offer Ad on TypingMind?

Because you install it once and use it with any model. Render Offer Ad 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 Offer Ad 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-offer-ad. 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 Offer Ad?

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 Offer Ad?

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

Is the Render Offer Ad 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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