Assemble a cinematic live-action-style music-video ad from a config — an original sung anthem carries the whole narrative while N 35mm-film-look i2v clips are each cut to their lyric window and hard-concatenated on the beat as a 3-act arc, the anthem muxed at loudnorm I=-14, cinematic lower-third serif captions built from the song's OWN word timings (never Whisper) with the hook line landing on the chorus drop, and closed on a brand end card composited from the real asset — never AI-rendered text. This is the FREE deterministic assembly stage (cut-to-window + hard concat + anthem mux + captions + end card); the anthem, keyframes, and clips come from create-music-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the cinematic-music-video format.
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Assemble a cinematic music-video ad from a config: a live-action-STYLE short film where an
original sung anthem is the score and every visual beat is a shot-on-film tableau (Kodak Portra
grain, light leaks, natural light, handheld imperfection) timed to the lyrics, arranged as a
3-act arc (its shape is the user's story_arc choice) with the hook line on the chorus drop. This capability is
the FREE, deterministic assembly — cut-to-window, hard-concat, anthem mux, caption burn,
and the brand end card.
scripts/config.example.json is the worked example (Hype and Vice "Game Day Girls", ~28s
1080×1920 9:16, 14 tableaux) — 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.
This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are
separate capabilities: the sung anthem (create-music-elevenlabs, force_instrumental false —
the lyrics ARE the script, returns mp3 + words_timestamps); one 35mm-film keyframe per beat
in one look pack (create-image-gpt-image-fal); and one Kling 3.0 i2v clip per beat (create-video-fal).
Given the delivered anthem + words.json + one clip per beat + the brand end-card asset,
render-cinematic-music-video cuts each clip to its lyric window, hard-concats on the beat,
muxes the anthem, burns the cinematic lower-third captions, and overlays the end card → the
master. Re-cuts reuse the existing anthem / keyframes / clips and cost $0.
The creative content this stage assembles is decided upstream by the format's choices, asked
of the user before any paid step. The worked example's values are examples, never defaults:
This assembly reads them only through the config (tableaux[] windows + captions, the anthem,
captions.accent_words, end_card.*); it hardcodes none of them.
force_instrumental false); do not add a spoken voiceover or a
second bed.words.json from the
music model's words_timestamps, chunk ~4 words at lyric boundaries, and burn cinematic
lower-third serif captions (--placement low) with the hook line accent-treated (bold-italic).
Whisper on sung audio returns "🎵 Music Playing 🎵".is_hook) is timed so the
load-bearing line sits on the chorus drop; accent that line in the captions.afade in/out + loudnorm I=-14), burn the caption ASS, overlay
the end card → a 1080×1920 h264+aac master. No paid calls, no keys.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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