跳到正文
cccskills
免费在 GitHub 公开

render-split-screen-creator

Assemble a split-screen creator ad from a config — a two-zone vertical composite where a supplied AI-creator lip-sync take fills the BOTTOM ~48% while real 16:9 product/demo clips run uncropped in the TOP ~52%, each top clip contain-fit with a darkened blurred cover-scale fill of the same clip (never black bars), a 3px brand-color divider between the zones, the creator slice cover-fit per the per-scene VO timing, scenes hard-concatenated with the body audio being the concatenated creator VO slices, an end card held on the last sharp frame ~3s, then the ASSEMBLED cut transcribed with local Whisper (not the raw VO — concat drops inter-scene silence) and word-level captions burned in the chosen style. This is the FREE deterministic assembly + caption stage (two-zone composite + blurred fill + divider + hard-concat + end card + captions); the VO comes from create-vo-elevenlabs, the anchor from create-image-gpt-image-fal, and the whole-VO lip-sync from a paid VEED Fabric 1.0 take (a no-atom upstream input). Use for the split-screen-creator format.

中文简介正在准备中。当前显示原文说明。

集成工具ElevenLabs
查看安装方法

包含的文件(6)

  • SKILL.md7.1 KB
  • scripts/config.example.json10.1 KB
  • scripts/PIPELINE.md6.1 KB
  • scripts/README.md3.9 KB
  • skill.meta.json353 B
  • tests/smoke-test.md1.5 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

render-split-screen-creator

Assemble a split-screen creator ad from a config: a two-zone vertical (1080×1920, 9:16, ~40s) format where an AI creator talking-head anchors the BOTTOM ~48% of the frame and real 16:9 product/demo clips run uncropped in the TOP ~52%, a 3px brand-color divider between the zones. The creator delivers the whole VO cold-to-camera and each top clip proves the claim its VO line makes. This capability is the FREE, deterministic assembly + captions — the two-zone composite (contain-fit + blurred-cover fill + divider + creator slice), the hard-concat, the end card, and the word-level caption burn from the assembled cut.

scripts/config.example.json is one worked example (Perplexity concept-10 "Bloomberg terminal", ~40s 1080×1920 9:16, 6 scenes + an end card) — 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 by the user (the calling format's recipe choices) and arrive here as config. This capability never picks them.

  • creator_gender / creator_age / creator_look — who the creator is (creator.brief); shapes the anchor and the voice upstream. Here it only affects framing checks. The worked example used a man in his 20s.
  • setting — where the creator films from (creator.brief.vibe_setting). The worked example used a lived-in room with window daylight.
  • tone — the script and VO delivery; upstream only. The worked example was a confident, fast explainer.
  • caption_style — captions.style (serif-accent, kinetic-pop, neon-glow, clean-bubble). Burned here. The worked example shipped serif-accent.

If a creative field is empty in the config, ask for it — don't fall back to the worked example's value.

Run

This is the FREE, deterministic assembly + captions stage — it spends nothing. The paid inputs are separate steps — the VO (create-vo-elevenlabs, ElevenLabs eleven_v3 with-timestamps, sliced into per-scene windows), the photoreal MEDIUM chest-up AI-creator anchor (create-image-gpt-image-fal, gpt-image-2) — shot at a natural webcam distance (headroom + shoulders, real room), not a plain-background close-up headshot (see the anchor note below), and the whole-VO lip-sync (a paid VEED Fabric 1.0 @ 720p take — a no-atom step, image_url = the anchor, audio_url = the vo mp3, ~$0.15/sec, ~$5.90 for a 39s VO; run its calls sequentially, veed/fabric-1.0 storage-auths 403 under parallel load). Given the creator lip-sync take + the per-scene VO timing + one 16:9 top clip per scene + the scene-1 hook graphic + the end-card clip, render-split-screen-creator composites the two zones, hard-concats the scenes, appends the end card, transcribes the assembled cut, and burns the captions → the master. Re-cuts reuse the existing VO / lip-sync / clips and cost $0.

Contract (the free assembly)

  • Two-zone split, top ~52% / creator ~48%. The TOP zone runs the real 16:9 product/demo clip contain-fit (uncropped), the BOTTOM zone is the creator lip-sync framed head-to-shoulders: scale-to-width × a small ZOOM (~1.15–1.2) then crop the zone with a downward offset so the face sits upper-middle and the shoulders enter the bottom. (A plain cover + crop-toward-top shows only the head and cuts the shoulders — and it can't rescue an anchor that was shot too close; fix the anchor distance first.) Tune zoom/offset visually against the source video's creator framing — it's a FREE re-assemble, no VEED re-run. A 3px brand-color divider separates the zones. Canvas 1080×1920, top_height ~998. Keep every stacked height EVEN (998 + 4 divider + 918 = 1920) — libx264 rejects odd dimensions.
  • Anchor = photoreal MEDIUM shot, not a studio headshot. The lip-sync only looks as good as the anchor. It must be photoreal/candid (real lived-in room, natural skin cues), framed chest-up at a natural webcam distance (headroom + shoulders), not a plain-background close-up and not a phone-selfie pose copied from another format (e.g. ugc-walk-and-talk). VEED Fabric handles photoreal fine (unlike Seedance).
  • Blurred-cover fill, never black bars. The top clip's letterbox margins are filled with a darkened blurred cover-scale of the same clip — a flat charcoal/black bar reads cheap.
  • One claim per scene, shown as it's said. Each top clip is windowed (top_start/top_end) to the on-message segment that proves its VO line. Never loop a short clip — set the window and the assembler speed-fits it to the scene (looping replays into a sparse/black tail).
  • The creator VO is the entire audio bed — no separate music. Body audio = the concatenated creator VO slices, timed per the per-scene timing.json; the lip-sync drives the mouth.
  • Hard-concat the scenes; end card on the last SHARP frame. Hard-cut concat (no dissolves); append the end card holding the last sharp frame ~3s. If the end-card clip fades to black, hold the last sharp second (endcard.clip_end), not the black tail.
  • Caption the ASSEMBLED cut, not the raw VO. Concat drops inter-scene silence, so the ad timeline ≠ the VO timeline; only the final cut's audio yields correct caption timing. Transcribe the assembled cut with local Whisper, build word-level cues (sentence-aware chunking), burn the ASS in the style set by captions.style (serif-accent, kinetic-pop, …). Keep the -precaption cut + the .ass sidecar so captions restyle without re-rendering the composite. 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.
  • FFmpeg composite, deterministic, FREE. Two-zone composite per scene, hard-concat, append the end card, mux the creator VO, loudnorm I=-14 → a 1080×1920 h264+aac master. No paid calls, no keys — the VEED Fabric lip-sync is a supplied input, produced upstream.

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-skills12632026年10月22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-skills12632026年10月22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-skills12632026年10月22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-skills12632026年10月22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-skills12632026年10月22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-skills12632026年10月22日 更新

查看 gooseworks-ai 的全部技能

报告此技能的问题