본문으로 건너뛰기
cccskills
무료GitHub에 공개

render-cartoon-music-video

Assemble a cartoon / animated / hand-crafted music-video ad from a config — a sung song carries the whole narrative while N per-bar i2v clips (one recurring animated character, one look pack) are each cut to their BAR window from librosa beat-tracking and hard-concatenated on the bar, VEED-whisper white bold-sans captions in the BOTTOM third (Alignment 2, above the logo bug, no pill) burned from the song's word timings re-spelled against the locked lyrics, a persistent brand logo bug held over the body (suppressed on the end card), and closed on a solid-brand-color PIL end card with the song still playing under it — never AI-rendered text. This is the FREE deterministic assembly stage (cut-to-bar + hard concat + logo bug + captions + end card + song mux); the song, character, keyframes, and clips come from create-music-elevenlabs / create-image-fal / create-video-fal. Use for the cartoon-music-video format.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

연동 도구ElevenLabs
설치 방법 보기

포함된 파일(6)

  • SKILL.md6.1 KB
  • scripts/config.example.json21.4 KB
  • scripts/PIPELINE.md8.4 KB
  • scripts/README.md4.5 KB
  • skill.meta.json351 B
  • tests/smoke-test.md1.0 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

render-cartoon-music-video

Assemble a cartoon music-video ad from a config: an animated / illustrated / hand-crafted story (stop-motion felt-and-foam, claymation, 2D toon, cut-out) where a sung song is the entire script and one recurring animated character carries every shot, cut to the bar with the climax line on the drop. This capability is the FREE, deterministic assembly — cut-to-bar, hard-concat, the logo bug + captions burn, and the solid-color end card.

scripts/config.example.json is the worked example (Coinbase "Bet on anything", ~55s 1080×1920 9:16, 16 body bars + a 3s end card — its felt look, character, story and song are that demo's picks, never defaults); scripts/PIPELINE.md maps every config block to its source step and scripts/README.md documents the free assembly.

Choices

The creative calls are the caller's (the video-format recipe asks the user); this assembly never picks them. The demo's value is an example only:

  • Art style / look pack (felt stop-motion, claymation, 2D toon, cut-out…) — demo: felt-and-foam diorama.
  • Character — the ONE recurring animated figure — demo: a gender-neutral felt figure in a brand-blue jersey.
  • Story arc — demo: underdog payoff.
  • Song style + lead vocal — demo: hype dance-pop / EDM-trap, male half-rapped lead.

Run

This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are separate capabilities: the sung song (create-music-elevenlabs, or a user-supplied mp3 + Whisper word timings) beat-tracked with librosa so the BAR GRID sets the timeline; one locked recurring character + one keyframe per bar in one look pack (create-image-fal, Nano Banana); and one Seedance i2v clip per bar (create-video-fal). Given the song + word-timestamps.json + bars.json + one clip per bar + the brand wordmark SVG, render-cartoon-music-video cuts each clip to its bar window, hard-concats on the bar, burns the logo bug + captions, appends the solid-color end card, and muxes the song under it → the master. Re-cuts reuse the existing song / keyframes / clips and cost $0.

Contract (the free assembly)

  • The sung song carries the narrative — no separate VO. The generated/supplied track IS the bed (no VO to duck under); do not add a spoken voiceover or a second bed.
  • Plan the timeline AROUND the delivered song's BAR GRID. Beat-track the song with librosa (assume 4/4); one bar = one shot (default). Snap every tableau window to the bar boundaries — never trim the song to a pre-planned grid.
  • Captions from the song's word timings, re-spelled against the locked lyrics. Whisper mishears shouted accents ("BETS" → "Hearts"); re-spell the timed tokens against the locked lyric file (never edit lyrics to match Whisper). VEED-whisper white bold sans in the BOTTOM third (Alignment 2, margin_v above the bottom-left logo bug), ~4–5 words per cue, NO background pill; captions STOP at the end-card boundary. Never mid-frame — it covers the character (fixed 2026-07). If the host ffmpeg lacks libass, render the cues as timed PIL PNG overlays (ffmpeg overlay=…:enable='between(t,st,en)') at the same bottom placement.
  • Land the climax line on the drop. The climax tableau is timed so the payoff line sits on the sub-bass drop; accent that line.
  • Persistent logo bug, suppressed on the end card. Burn a white brand wordmark bottom-left over the body bars (cairosvg → PIL from the real SVG), suppressed on the end card where the big wordmark dominates.
  • End card via cairosvg + PIL from the real wordmark — never AI-render brand text. Solid brand-color card + white wordmark + subhead, holding ~3s WITH the song still playing under it (afade-out over the tail — no silent tail). A diffusion model garbles a wordmark.
  • FFmpeg composite, deterministic, FREE. Cut each clip to its bar window, hard-concat, burn the logo bug + captions, append the end card, mux the song over the whole video with a 0.5s afade tail, loudnorm I=-14 → a 1080×1920 h264+aac master. No paid calls, no keys. When the host ffmpeg lacks libass/drawtext, BOTH the captions and the logo bug are timed PIL PNG overlays (overlay=x:y:enable='between(t,st,en)'), not an ASS burn.
  • QC PER SCENE, never just the master — the two failure modes are content, not assembly. The upstream keyframe/i2v steps can (a) drift the character off its look (felt → smooth-3D "man" in the demo) partway through (off the chosen look — in the felt demo, felt → smooth-3D) and (b) hallucinate hands — realistic fingers in a hand macro, a pointing finger on a "tap the phone" shot, or a disembodied hand sliding in from the frame edge. Both hide at thumbnail size. Extract a 2 fps contact sheet + a per-bar FACE crop and HAND crop (across each clip's full duration), confirm every bar holds the ONE chosen look (the demo: matte felt) with the ONE character and no human/floating hands, and re-check the served bytes after publish. A drifted bar means regenerating that bar's KEYFRAME (not re-cutting) — see the recipe STEP 3/6.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

Research supported advertising promises from a specific buyer's context, current product facts, customer language and observed ads. Turn them into static, video or copy ideas; match video ideas to actual recipes and save a product-scoped angle bank for write-video-ad-script. Product-led discovery can use verified facts without reviews. Reference persistence and reach are observations, not proof of performance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

Analyze ad campaign performance data (Google, Meta, LinkedIn) to identify what's working, what's wasting budget, and specific cut/scale/test recommendations. Runs statistical analysis, funnel diagnostics, and multi-channel budget reallocation with specific dollar-amount shift recommendations and scenario modeling.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

For paid lead-gen and participant-recruitment ads, replaces vanity CPA with true CAC per qualified lead by joining ad-platform data with downstream funnel events, surfaces tracking gaps, and classifies every creative into Scale / Keep / Investigate / Cut.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

The intake stage of a Meta ad harness. Researches the brand first, states what it assumes, and asks only what it cannot infer and what would change money, claims or legal exposure if guessed wrong — at most four questions a turn, in plain business language. Writes the brand's stable facts (ads/brand.md), the campaign brief, and a log of what was asked vs assumed. Use it when someone wants to run Meta ads and the brand's ad facts are not yet recorded.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

Analyze the message match between your ads and landing pages. Checks if the promise in the ad copy carries through to the landing page headline, body, and CTA. Flags disconnects that kill conversion rates. Works with Google, Meta, and LinkedIn ads.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

gooseworks-ai/goose-skills1.3천2026년 10월 19일 업데이트

gooseworks-ai의 스킬 모두 보기

이 스킬의 문제 신고