跳到正文
cccskills
免费在 GitHub 公开

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.

中文简介正在准备中。当前显示原文说明。

实测评分 未实测

查看安装方法

包含的文件(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(原文)

安装前可以查看交给 AI 代理的指令内容。

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.

评价

还没有评价。欢迎分享你的使用感受。

评论

无需登录即可发表。欢迎分享使用感受、提问,或说明在哪些环境下无法运行。

还没有评论。来写第一条吧。

提交后会立即公开。请勿填写邮箱、电话等个人信息。URL 最多 2 个。

0 / 1000 字

此表单受 reCAPTCHA 保护,适用 Google 的隐私权政策和服务条款。

同一仓库的技能

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.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

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.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

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.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

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.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

Checks paid ads against the ad platform's rules (Meta, TikTok, Google, LinkedIn and others) and the brand's category rules: the copy, on-image or on-screen text, pictures and people. Rewrites what can be fixed honestly, adds the disclosures that are needed, and flags anything that needs a human or legal review, with one pass, fixed or flag line per ad and platform. Use alone when you already have the ad text or images and know where they will run; otherwise run the playbook for the job (e.g. make-static-ads, make-content, email-lifecycle).

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

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.

中文简介正在准备中。当前显示原文说明。

gooseworks-ai/goose-skills12652026年10月24日 更新

查看 gooseworks-ai 的全部技能

报告此技能的问题