Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video — birefnet cutout, then a cold-open text card + product carousel + annotated specimen-sheet end card, plus loudnorm + separate-pass music mux. FREE assembly (PIL + rsvg + FFmpeg); the recipe supplies the config and gates the paid BG/cutout/music calls to their own capabilities. Use for the vignette format.
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Assemble a short-form 'vignette' ad from clean product cutouts composited over a kinetic background video. Motion lives in the BG video; the product rides on top as a static cutout layer. Music-led, zero VO, sub-12s, loopable — it reads muted because the product label + on-screen copy carry the message. Defaults to the V-CARD structure: a cold-open text card → a product carousel under one shared BG → an annotated specimen-sheet end card.
strip_product_backgrounds.py — birefnet cutout of each PDP shot to clean hard-edge alpha (no halo/shadow).render_overlays.py — PIL + rsvg-convert render the cold-open card (cold_open_text in cold_open_font, dead-center) + the annotated specimen-sheet end card (brand SVG logo + end_card.lines annotations) as transparent 1080x1920 PNGs. Text colour follows end_card.logo_variant. FREE.composite_variants.py — one FFmpeg filter_complex per BG variant: BG (palette-aware dim) → cold-open overlay → cutouts (width-anchored, vertically centered y=(H-h)/2) → end card. h264 crf20 yuv420p +faststart 30fps. FREE.music_and_mux.py — instrumental music bed from config.music.prompt → acompressor + loudnorm I=-18:TP=-2:LRA=9 → muxed into every variant in a SEPARATE pass with explicit -map 0:v:0 -map 1:a:0. The mux is FREE; the music generation is a paid call that in prod routes through create-music-elevenlabs.Every script reads config.json (--config <path>, $VIGNETTE_CONFIG, <project>/config.json or scripts/config.json) and exits naming any missing required field — there are no built-in demo values.
The recipe asks the user these before any paid step; this atom only reads the result from config. The demo build's value is an example, never a default.
bg_motif — the kinetic background's subject. Arrives as bg_concepts[].brief (BG sourced upstream). Demo: chrome molecular swarm / black ink blooming in cream.bg_palette — the background's colour feel. Arrives as bg_concepts[].dim (heavy/light dim) + end_card.logo_variant (logo + text colour). Demo: cream with chrome + ink accents.music — the instrumental bed's feel. Arrives as music.prompt (no brand names — ElevenLabs rejects them). Demo: slow ambient minimal electronic ~90 BPM.y=(H-h)/2) so mixed-shape SKUs share one visual mid-line — never bottom-anchor (squat jars jump). For 9:16 scale by WIDTH (~75% tall bottles, ~65% squat jars).saturation=0.50); naturally-contrasty BG → lighter dim (saturation=0.85).-map 0:v:0 -map 1:a:0 (single-pass composite+mux silently ships 1 kbps garbage audio).strip_product_backgrounds.py calls fal-ai/birefnet/v2 directly via a fal_helpers shim (sys.path.insert into a shared atoms dir + from fal_helpers import ...). It is bundled here because the cutout is an intrinsic assembly step, but in prod the birefnet cutout should route through create-image-fal (the fal-proxy capability that bills the Ads agent) rather than hitting fal.run directly. Treat the direct-fal path as a gap to close; the recipe gates the cutout to gooseworks fetch create-image-fal.source/t2v-outputs/<slug>.mp4 so the composite is source-agnostic. There is no Pexels fetcher bundled here; that lives in the recipe's playbook.music_and_mux.py also uses the fal_helpers shim for the ElevenLabs music generation; in prod that generation routes through create-music-elevenlabs, and only the loudnorm + separate-pass mux run locally as FREE assembly.还没有评价。欢迎分享你的使用感受。
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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.
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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.
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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.
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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.
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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).
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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.
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