本文へ移動
cccskills
無料GitHub で公開

render-cinematic-music-video

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.

インストール方法を見る

含まれるファイル(6)

  • SKILL.md5.1 KB
  • scripts/config.example.json23.6 KB
  • scripts/PIPELINE.md9.9 KB
  • scripts/README.md2.9 KB
  • skill.meta.json355 B
  • tests/smoke-test.md1.2 KB

SKILL.md(原文)

インストールする前に、エージェントに与えられる指示の中身を確認できます。

render-cinematic-music-video

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.

Run

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.

Choices

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:

  • song_style — the anthem's genre, mood, BPM and arrangement. The demo used a triumphant 120-BPM indie-pop cinematic anthem.
  • vocalist — who sings it. The demo used a confident-but-warm female lead.
  • cast — who is on screen in the tableaux. The demo used four young college women.
  • setting — place + time of day (drives the look pack's light and palette). The demo used an American college town on game day, autumn, golden hour.
  • story_arc — the 3-act shape of the tableaux and lyrics. The demo used one game day, morning → stadium peak → twilight, with an origin-story wink.

This assembly reads them only through the config (tableaux[] windows + captions, the anthem, captions.accent_words, end_card.*); it hardcodes none of them.

Contract (the free assembly)

  • The sung anthem carries the story — no separate VO. The generated ElevenLabs track IS the bed and the script (force_instrumental false); do not add a spoken voiceover or a second bed.
  • Plan the timeline AROUND the delivered anthem. The anthem is generated first and reshapes/overshoots length; snap every tableau boundary to the lyric-phrase edges in the returned word timings — never trim the anthem to a pre-planned grid.
  • Captions from the anthem's OWN word timings, not Whisper. Derive 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 🎵".
  • Land the hook on the chorus drop. The hero tableau (is_hook) is timed so the load-bearing line sits on the chorus drop; accent that line in the captions.
  • One cinematic look pack + hard cuts on the beat. The look pack (named film stock + grain + flares + handheld) plus a continuity anchor holds every clip together; cut each clip to its lyric window and hard-concat (one optional match-cut into the hero reveal) — no dissolves.
  • End card from the real brand asset — never AI-render the wordmark as diffusion text. Composite the lockup (PIL/ffmpeg) from the real asset or use a designed keyframe base; diffusion garbles a wordmark.
  • FFmpeg composite, deterministic, FREE. Normalize each clip to its beat-locked window, hard-concat, mux the anthem (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.

レビュー

まだレビューはありません。使ってみた感想をお寄せください。

同じリポジトリのスキル

概要と使いどころ

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.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13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.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13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.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13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.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13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.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.

日本語の概要は準備中です。原文の説明を表示しています。

gooseworks-ai/goose-skills1,2452026年10月13日 更新

gooseworks-ai のスキルをすべて見る

このスキルの問題を報告する