Skip to content
cccskills
FreePublished on GitHub

render-3d-product-showcase

Assemble a vertical product showcase ad from prepared clips, a brand wordmark, and an instrumental track. The skill trims and normalizes four beats, adds a deterministic brand end card, joins them in order, and mixes the music into a finished video.

OriginalShow the original description

Assemble a premium 3D product-showcase ad from a config — four beat clips (an orbiting hero rotation, a macro push-in, a physics reveal, a typographic close) normalized to the chosen backdrop colour, hard-concatenated in order, closed on a deterministic Playwright brand end card, and mixed under one instrumental bed at loudnorm I=-16 (music-only, no VO). Ships the runnable build_endcard.py + build_masters.py; the rotation/macro clips are create-video-fal i2v seeded on a create-image-fal styled hero, the reveal is Veo3 i2v, and the bed is create-music-elevenlabs. Use for the 3d-product-showcase format.

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

Run score Not tested

See how to install

Good for

  • Assembling a product ad from generated clips
  • Adding a branded end card to a video
  • Re-cutting an existing showcase without new generation
  • Creating a music-only vertical product video

Files included (9)

  • SKILL.md4.9 KB
  • scripts/build_endcard.py5.8 KB
  • scripts/build_masters.py4.8 KB
  • scripts/config.example.json10.8 KB
  • scripts/PIPELINE.md5.6 KB
  • scripts/README.md3.8 KB
  • scripts/shoot.js1.2 KB
  • skill.meta.json236 B
  • tests/smoke-test.md1.1 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

render-3d-product-showcase

Assemble a premium 3D product-showcase ad from a config: one real product floats centered on a clean seamless backdrop (colour and lighting chosen per brand) and sells itself across four beats — an orbiting hero rotation, a macro push-in on the surface detail, a physics reveal (exploded_view / particle_disintegration / liquid_splash, or rotation_only), then a typographic brand close. This capability is the FREE, deterministic assembly that stitches the delivered beats into the master; it spends nothing.

scripts/config.example.json is the worked example (Quillmere Pen Co. "Meridian 14", ~15s 720×1280 9:16); scripts/build_endcard.py + scripts/build_masters.py are the runnable free assembly; scripts/PIPELINE.md maps every config block to its source step; scripts/README.md documents the assembly.

Choices

The recipe asks these before any paid step; this capability only assembles their result. The demo's value is an example, never a default:

  • backdrop → studio_look.bg (the pad colour build_masters.py uses) + the beat prompts. Demo: deep teal #1F4E5A.
  • lighting_mood → studio_look.lighting_preset / rim_light + the beat prompts. Demo: luxe_botanical_warm (warm rim light upper-right, warm bokeh).
  • reveal → reveal_variant + beats[2]. Demo: exploded_view.
  • music → music.prompt (the bed passed to build_masters.py --music). Demo: minimal ambient-electronic, drop at 7s, 100 BPM.

Run

This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are separate capabilities: Beats 1 & 2 (hero rotation + macro push-in) are create-video-fal image-to-video seeded on a create-image-fal styled hero — a nano-banana restyle of the brand's REAL product photo onto the seamless studio set, so the product geometry is real and never AI-invented; Beat 3 (the physics reveal) is a Veo3 image-to-video seeded on Beat 1's last frame (also create-video-fal); the one instrumental bed is create-music-elevenlabs. There is no Higgsfield / Marketing Studio in this format — everything paid runs through the fal-proxy / elevenlabs-proxy so the call is charged to your GooseWorks credits.

Given those beats + the brand wordmark, this capability:

  1. build_endcard.py --bg beat1_last_frame.png --headline "…" --wordmark <wordmark> --out endcard.png — the deterministic Beat-4 hyperframe (Playwright 1080×1920 → ffmpeg-scale to 720×1280).
  2. build_masters.py --config config.json --clips working/clips --endcard endcard.png --music music.mp3 --out master.mp4 — trims each beat to its window, normalizes to the brand canvas, hard-concats, mixes the bed.

Re-cuts reuse the existing beats and spend nothing.

Contract (the free assembly)

  • Music-only, no VO. One ElevenLabs instrumental bed carries the film; nobody speaks. Do not add a spoken voiceover or a second bed.
  • Beat 1's last frame is the shared anchor. Extract it (ffmpeg -sseof -0.1 … -frames:v 1) once — it seeds the Veo3 Beat 3 AND backs the Beat 4 end-card hyperframe, so the product + lighting carry across all four beats and the geometry never AI-drifts.
  • End card via Playwright from the real wordmark — never AI-render brand text. The brand close is a deterministic hyperframe (Beat 1 last frame + scrim + serif headline — --headline-font, Playfair Display by default — + the real wordmark, recolored for contrast). build_endcard.py auto-picks a legible headline color from the bg luminance and renders 1080×1920 then scales to 720×1280. The headline font is loaded from Google Fonts; bundle the .ttf if determinism offline matters. If the resolvable Playwright wants an uninstalled browser build, export PW_CHROME=<installed Chromium binary> (shoot.js honours it).
  • Normalize each beat to the brand canvas, hard-concat. Per beat: trim to the window, strip the i2v model's auto-audio (-an), scale + pad to 720×1280 with the brand bg pad color, 24fps, yuv420p, crf 18 → concat demuxer. No dissolves.
  • FFmpeg mix, deterministic, FREE. Mix one instrumental bed (afade in/out + loudnorm I=-16 TP=-1.5 LRA=11, apad + atrimmed to master duration) over the concatenated beats → a 720×1280 h264+aac master. No paid calls, no keys.

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