본문으로 건너뛰기
cccskills
무료GitHub에 공개

render-vo-anchored-motion-listicle

Assemble a narrated motion-graphic LISTICLE video ad from a config — a spoken voiceover (cloned or cast, in the chosen tone) carries a numbered listicle while N web-animated hyperframe beats (HTML plus the Web Animations API, one branded design system of alternating tiles, big hero numerals, and callouts in the chosen visual style) are rendered frame-by-frame via Playwright and anchored to the VO's word-level timestamps, optional color-graded breather windows (brand clip, AI clip, or motion-graphic) give visual breath, and captions burn ONLY inside those B-roll windows (2-word chunks, ASS Format header carrying a Name field so none drop) with the VO mixed under a low music bed. This is the FREE deterministic assembly stage (Playwright beat render plus ffmpeg concat plus window-masked caption burn plus VO-and-music mix plus final composite) — the VO, the music bed, and any AI breather clips come from create-vo-elevenlabs, create-music-elevenlabs, and create-video-fal. Use for the vo-anchored-motion-listicle format.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(6)

  • SKILL.md6.5 KB
  • scripts/config.example.json9.7 KB
  • scripts/PIPELINE.md7.1 KB
  • scripts/README.md3.8 KB
  • skill.meta.json367 B
  • tests/smoke-test.md1.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

render-vo-anchored-motion-listicle

Assemble a narrated motion-graphic listicle ad from a config: a spoken voiceover carries a numbered listicle (hook + N points + CTA) and every visual beat is anchored to the VO's word-level timestamps. Each beat is a web-animated hyperframe (an HTML page + the Web Animations API driven by window.renderAt(t)) rendered to video frame-by-frame with Playwright, all in ONE branded design system (alternating background tiles, big hero numerals, body type, decorative accents, callouts). Optional color-graded breather windows give visual breath, and captions burn only on the B-roll windows. The shipped master is pure motion-graphic + VO — there is NO lipsync (the still headshot is kept only for a future lipsync variant). This capability is the FREE, deterministic assembly — the Playwright beat render, the ffmpeg concat, the window-masked caption burn, the VO+music mix, and the final composite.

scripts/config.example.json is the worked example (Everself "doctor-educator" listicle, ~66s 1080×1920 9:16 at 25fps) — copy its structure, never its creative values; scripts/PIPELINE.md maps every config block to its source step and scripts/README.md documents the free assembly.

Choices

The creative calls are made upstream (the format recipe's choices, asked of the user) and arrive in the config; this assembly just renders what it is given. The demo's values are examples only.

  • narrator — who voices it (cloned real person or cast voice) → voice.mode / voice.voice_id. The demo used a cloned doctor-educator.
  • tone — how the read sounds → the script + the VO. The demo used authoritative expert.
  • list_angle — mistakes + fix, tips, myths vs facts, signs you need X… → the script + beats[]. The demo used expert tips with mistake → fix beats.
  • visual_style — the motion-graphic look in brand colours → design.name / callout_style / accents / background_tiles. The demo used EDUCATOR_MOTION with glass-pill callouts.
  • music — the bed style → music.prompt (none = mix the VO alone). The demo used a warm lo-fi bed at ~0.18.

Run

This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are separate capabilities — the spoken VO (create-vo-elevenlabs, a cloned or cast voice, eleven_v3 + atempo) whose word-level timestamps (Groq whisper-large-v3 word-level) set the timeline; the low music bed (create-music-elevenlabs); and any AI breather clips (create-video-fal, trimmed + color-graded; brand clips and motion-graphic breathers are free). There is no free stock-footage source. Given the VO + words-flat.json + the N authored hyperframe beats + the color-graded B-roll windows + the brand wordmark SVG, render-vo-anchored-motion-listicle renders each beat frame-by-frame via Playwright (all beats at fps 25), concats the beats + B-roll, burns the window-masked captions, mixes the VO under the low music bed, and composites → the master. Re-cuts reuse the existing VO / beats / B-roll and cost $0.

Contract (the free assembly)

  • The spoken VO carries the listicle — it sets the timeline. The cloned/cast VO is the spine; Whisper-transcribe it to word-level timestamps and anchor every beat reveal to those word times. There is no on-camera human and NO lipsync in the shipped master.
  • Beats are web-animated hyperframes, rendered deterministically. Each beat is an HTML page + the Web Animations API driven by window.renderAt(t); Playwright screenshots it frame-by-frame and ffmpeg encodes it. This is NOT i2v — it is deterministic web motion graphics.
  • ALL beats share the same fps (25). A mismatched-fps beat stutters at the concat seam. Render every beat and every B-roll window at fps 25.
  • ONE design system across every beat. A single _shared.css (palette + type + alternating tiles + accents + callout style) so N beats read as one designed reel; alternate only the background tile, keep numerals / body / accents / callouts consistent.
  • Periodic color-graded B-roll windows for breath. Trim + color-grade each breather (brand clip, AI clip, or motion-graphic) to the palette (fps 25). These windows are the ONLY captioned windows.
  • Captions ONLY on the B-roll windows. On the motion-graphic beats the on-screen type IS the caption — burning Whisper captions there double-stacks text. Build the caption ASS from the VO word timings, kept only inside the B-roll windows, 2-word chunks, closing a cue on any >0.4s word gap. The ASS Format: header MUST carry a Name field — without it the leading-comma bug eats the first field and captions silently drop. 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.
  • Low music bed under the VO. The VO is the spine; the ElevenLabs Music bed (style from the music choice; skipped if none) sits ~0.18 vol under it. No ducking needed at that level.
  • Never AI-render the brand lockup. The CTA / end beat composites the brand's real wordmark, never text-in-diffusion.
  • FFmpeg composite, deterministic, FREE. Render each beat via Playwright, concat the beats + B-roll (ffmpeg demuxer), burn the window-masked caption ASS, mix the VO under the music bed, loudnorm I=-14 → a 1080×1920 25fps h264 crf18 + aac 192k master. No paid calls, no keys.

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