Skip to content
cccskills
FreePublished on GitHub

render-cgi-sizzle

Assemble a vertical 3D CGI app showcase using real App Store screenshots composited onto a rendered phone, then add motion, voiceover, and music. The provided config and pipeline guide the workflow; paid image, video, and audio generation steps are separate capabilities.

OriginalShow the original description

Assemble a 3D-CGI app sizzle — nano-banana CGI plates (blank-glow floating phone + a CGI studio with rim-light + placeholder burst shapes; studio look and phone finish come from the recipe choices) plus PIL compositing of the REAL App Store screenshots onto the bezel + burst-out overlays, driven by Kling 3.0 i2v steady-float per beat with a per-beat Ken-Burns FFmpeg push-in fallback when Kling garbles the UI, then VO/music mix (sidechain duck, loudnorm) + 1.15x speed + anti-AI grain finalize. The on-screen UI, any faces in it, and the wordmark are ALWAYS real assets composited via PIL — never AI-rendered. The paid steps (plates, i2v clips, VO, music) are separate capabilities; this ships the config + PIPELINE + FREE assembly and the recipe orchestrates the spend. Use for the cgi-app-sizzle video format.

gooseworks-ai/goose-skills1,272 stars(GitHub 1,272 + cccskills 0)
Works withElevenLabs

Run score Not tested

See how to install

Good for

  • Create a CGI showcase for a mobile app
  • Present five or six app features in a short video
  • Composite real App Store screens into a rendered phone
  • Prepare a vertical product film with voiceover and music

Files included (6)

  • SKILL.md5.5 KB
  • scripts/config.example.json13.2 KB
  • scripts/PIPELINE.md4.1 KB
  • scripts/README.md4.4 KB
  • skill.meta.json172 B
  • tests/smoke-test.md1.4 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

render-cgi-sizzle

Assembles the 3D-CGI app sizzle video format: a phone floats in a CGI studio while five or six app features demo one per beat — each beat bursts REAL App Store UI elements out of the phone in 3D with rim-light + bokeh, then everything collapses back into the screen at a value-prop climax + a brand end card over a music bed. It reads as an Apple-keynote product film, not UGC and not a physical-product shoot.

The worked example (scripts/config.example.json) is the fictional trail-map app Trailvane: a gold phone in a smoky-black studio with an amaranth rim-light, an "Eryn" keynote-cadence VO, a premium-tech piano/synth bed, and an "Every trail worth walking. One app." climax. Those are one example, not defaults.

Choices

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.

  • studio_look — background, rim-light colour, bokeh; also the end-card canvas + bar. Asked of the user; the demo used smoky-black #0A0A0A + amaranth #E32652 rim-light.
  • phone — the phone model/finish in every plate. Asked; the demo used a gold/champagne titanium iPhone 15 Pro.
  • narrator_voice — the VO voice. Asked; the demo used ElevenLabs "Eryn".
  • vo_delivery — how the VO is written and read. Asked; the demo used a restrained Apple-keynote spec-sheet cadence.
  • music — the bed's genre/mood. Asked; the demo used a premium-tech underscore.

This capability ships the recipe (scripts/config.example.json) + a config→step map (scripts/PIPELINE.md) + the FREE-assembly how-to (scripts/README.md). It documents the FREE, deterministic assembly between the paid model calls:

  • nano-banana CGI plates (paid, separate cap) render only the phone shell + the chosen studio + rim-light + bokeh + placeholder burst shapes; the screen is left a blank warm glow on purpose, and every plate is --anchored on beat 1 so the phone/studio stay identical across beats.
  • PIL real-UI compositing (FREE) — auto-detect the bright phone-screen bbox in each plate and feather the REAL App Store screenshot into the bezel → scene-NN-composite.png; bake real burst-out overlays (e.g. climax portrait tiles, rim-light baked before rotation) around the plate. The on-screen UI + faces + wordmark are ALWAYS real assets — never AI-rendered, so no claim is invented and nothing reads fake.
  • Kling 3.0 i2v steady-float (paid, separate cap) drives each composite; the burst-out pops/settles while the phone + screen stay locked. Any beat Kling garbles drops to a FREE Ken-Burns FFmpeg push-in (zoompan, heavier on the climax) — the shipped demo used this path for the feature beats.
  • Assembly + finalize (FREE) — dice + intercut concat (timeline locked from the measured VO durations), audio mix (sidechain-duck the music under VO, loudnorm -14 LUFS master), PIL brand end card (real wordmark, never AI), then 1.15x speed + anti-AI grain master.

See scripts/README.md for the full FREE-assembly detail and scripts/PIPELINE.md for the config-field → source-step map.

Run

Config-and-PIPELINE capability (no re-built runnable pipeline here). Copy the structure of scripts/config.example.json → config.json, fill the creative fields from the recipe choices (never the demo's values), rebuild the brand/beats/screens, and follow scripts/PIPELINE.md:

VO first (locks the timeline) → nano-banana CGI plates → PIL screen composites → burst-climax overlays → Kling i2v clips (Ken-Burns fallback per garbled beat) → PIL end card → captions → sidechain-ducked mix → 1.15x speed + grain.

Output: 1080x1920, ~22s H.264 (+ AAC music). 5-6 feature beats + PIL end card.

Contract

  • REAL UI, always PIL — never AI. Every app screen, any face in it, and the wordmark is the real asset composited via PIL. AI plates only ever render the phone shell, studio, bokeh, and placeholder burst shapes. This is the format's whole credibility and the guard against invented claims.
  • Kling for the float, Ken-Burns fallback per beat. If a Kling beat garbles the burst-out UI, distorts the phone, or animates the screen, fall that beat to a FREE Ken-Burns push-in — never ship a garbled beat.
  • Anchor every plate on beat 1 so the phone/studio read identical across beats (one shoot).
  • Timeline locked from the measured VO durations, never planned word counts.
  • The paid steps — nano-banana plates, Kling 3.0 i2v beats, ElevenLabs 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.

Reviews

No reviews yet. Tell others what you thought.

Comments

No login needed. Share how it worked for you, ask a question, or note where it did not run.

No comments yet. Be the first to write one.

Comments are published right away. Do not include personal information such as email addresses or phone numbers. Up to 2 URLs.

0 / 1,000 characters

This form is protected by reCAPTCHA, and the Google Privacy Policy and Terms of Service apply.

More skills from this repository

<slug>

FreeSummary

The supplied material contains only placeholder text for the skill’s purpose, inputs, steps, and completion criteria. It does not provide enough information to describe a task or result reliably.

  • No use case can be identified from the supplied material
  • Purpose is unspecified
  • Inputs and expected results are unspecified
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

ad-angle-miner

FreeSummary

Research supported advertising angles using buyer context, current product facts, customer language, and observed ads. Turn the evidence into static ad, video, or copy ideas, matching video concepts to available recipes. Scores rank ideas for testing; they do not predict results.

  • Finding evidence-backed angles for a product campaign
  • Developing static ad or video concepts from customer research
  • Writing headline and primary-text options for ads
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Analyze campaign data from Google, Meta, and LinkedIn to find strong and wasteful spend. The skill checks funnel performance and test results, then recommends which campaigns to pause, scale, or test and how to shift budget across channels.

  • Finding campaigns and ads that waste budget
  • Comparing performance across Google, Meta, and LinkedIn
  • Planning specific budget shifts and follow-up tests
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Compare ad spend and signups with downstream outcomes to calculate the cost of each qualified lead. The skill checks tracking coverage, accounts for how long leads need to mature, and groups creatives into actionable categories with confidence notes.

  • Compare true qualified-lead costs across creatives
  • Find low-CPA ads that bring poor-quality leads
  • Check whether signups can be linked to downstream outcomes
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

Prepare the intake for a Meta ad campaign by reviewing available brand information first, stating reasonable assumptions, and asking only questions whose answers could affect budget, claims, or legal exposure. It records stable brand facts, a campaign brief, and what was asked versus assumed.

  • Prepare a Meta ad campaign when brand facts are missing
  • Clarify budget and timing before campaign planning
  • Record approved claims and audience restrictions
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

ad-policy-check

FreeSummary

Review ad copy, image text, visuals, and people against platform and brand category rules. It suggests honest fixes and needed disclosures, then flags issues requiring human or legal review, with a separate result for each ad and platform.

  • Checking ad copy and visuals before launch
  • Reviewing creator ads for required disclosures
  • Fixing a platform disapproval and recording the issue
gooseworks-ai/goose-skills1.3KUpdated Oct 27, 2026

See all skills by gooseworks-ai

Report a problem with this skill