跳到正文
cccskills
免费在 GitHub 公开

render-airdrop-carousel

Assemble a viral iOS "AirDrop" notification-carousel video ad (≈6–8s, 9:16) from a brand line plus 6–16 real product photos — a native AirDrop share-sheet card ("Brand would like to share a ___ · Decline / Accept") springs up and its preview window CYCLES through the products, landing on a range/lineup payoff with an Accept tap; a chime + soft per-swap ticks track the swaps. DETERMINISTIC assembly — an HTML card (real DOM text) rendered to PNG via headless Chrome, chroma-keyed, its magenta window refilled per-product in PIL, then animated + audio-synthed with FFmpeg. FREE (no paid model calls); the recipe supplies the brand line, product images, and payoff and gates the only optional paid step (a hero shot when the brand has NO usable photo → create-image-fal). Use for the airdrop-notification-carousel format.

中文简介正在准备中。当前显示原文说明。

集成工具Playwright

实测评分 未实测

查看安装方法

包含的文件(7)

  • SKILL.md5.4 KB
  • scripts/build_card.py5.0 KB
  • scripts/compose_carousel.py9.3 KB
  • scripts/config.example.json1.5 KB
  • scripts/one_shot.py4.4 KB
  • skill.meta.json345 B
  • tests/smoke-test.md4.2 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

render-airdrop-carousel

The free, deterministic renderer for the airdrop-notification-carousel video ad format — the viral iOS "AirDrop" trend where a native share-sheet card ("Brand would like to share a candle · Decline / Accept") springs up on the phone and its preview window flips through a carousel of real product photos, landing on a range/lineup payoff with an Accept tap.

This is a DETERMINISTIC composite — no generative video, no AI-rendered product. A real-DOM AirDrop card is rendered once to PNG (headless Chrome), chroma-keyed, and its preview window is refilled per-product in PIL, then animated + audio-synthed with FFmpeg. The whole point of the format is crisp system-UI text and real product photography, both of which a video model would smear. This capability makes no paid calls; the recipe gates the only optional paid step — generating a hero product shot when the brand has NO usable photo at all (→ create-image-fal).

Default output ≈ 6–8s, 1080×1920, h264 + aac. Duration is DERIVED, not trimmed — first_hold + (N-1)·per + final_hold. Add images or raise per to lengthen.

Scripts (free)

  • scripts/build_card.py — brand params → chrome.html + chrome-pressed.html: the AirDrop card on a green page (#00e000) with a magenta preview window (#ff00ff) and a solid brand band (wordmark SVG or text + tagline). Real DOM text — AirDrop, Decline, Accept, and the brand line are DOM/SVG, never AI-rendered.
  • scripts/one_shot.py — glue: build_card → headless-Chrome (Playwright) fullPage screenshot of both card states → compose_carousel. One --config, one MP4.
  • scripts/compose_carousel.py — the render engine: green-key the card → detect the magenta window → fill it per-product (cover-crop) → blurred per-product backdrop + push-in → card spring-up (iOS ease-out-back) + carousel + Accept tap → synth audio (whoosh on entry, chime on land, a tick per swap, a pop on the tap) → encode h264+aac.
  • scripts/config.example.json — the shape of the brand config the recipe binds (brand-neutral worked defaults; replace every /abs/path/... placeholder).

Inputs the recipe binds (choices vs brand facts)

  • From the user's choices (asked by the recipe, never defaulted): the share message, the product images + order, the final_image payoff, and timing.per (pace).
  • From the brand kit: brand, wordmark_svg, tagline (a real stated proof point only), accent and band_color. If not passed, the scripts fall back to a neutral iOS blue #0A84FF / light grey #F2F2F7 — pass the brand's own colors.

Chroma contract (load-bearing — build_card.py and compose_carousel.py MUST share it)

#00e000 green = page background, keyed to the card's alpha. #ff00ff magenta = preview window, replaced per product. Neither color may appear in the card art or any product photo — a product image containing near-pure green or magenta gets keyed/misread. Swap the image or tighten the window_mask thresholds if it bleeds.

Craft rules (faithful to the source molecule)

  • Real product photos only. The format's credibility is that these look like real AirDropped items — no AI-generated products in the carousel.
  • Never AI-render text — the card UI, wordmark, and band are DOM/SVG composited so they stay pixel-crisp. Cover-crop the window; white-bg PDP shots reading as "product in a white tile" is on-brand and fine.
  • 6–16 images, ordered; end on the range — the payoff (final_image) should read as "the whole line" (lineup / family / all-shades).
  • Hard cuts on rhythm, a tick per swap — do NOT crossfade.
  • Never invent proof — a 1M+ sold tagline must be the brand's OWN stated figure, not filled from a brand-kit proof section.
  • Duration is derived, not trimmed — add images or raise per; never cut the audio.

Requires

  • Python 3 with numpy + Pillow, and ffmpeg/ffprobe on PATH.
  • Playwright chromium for the screenshot step (pip install playwright && playwright install chromium). If Playwright is unavailable, run build_card.py, screenshot chrome.html / chrome-pressed.html (fullPage) → chrome-green.png / chrome-green-pressed.png via the chrome-devtools MCP, then call compose_carousel.py directly.
  • watch (QC the final master). The recipe gates create-image-fal (optional hero shot when the brand has NO usable product photo) — the only paid, proxy-routed step.

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-skills12642026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-skills12642026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-skills12642026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-skills12642026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-skills12642026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-skills12642026年10月24日 更新

查看 gooseworks-ai 的全部技能

报告此技能的问题