Render song mv
Skill gooseworks-ai/goose-skills/skills/ads/capabilities/render-song-mv
Assemble a song-driven music-video ad from a config — a generated sung track carries the whole narration across N tableaux (one keyframe -> one i2v clip per lyric beat) with NO separate voiceover, captions synced to the song's OWN word timings (script-window, never Whisper) and the hook word landing on the chorus drop, closed on a PIL brand end card. This is the FREE deterministic assembly stage (clip cut-to-timeline + captions + end card + FFmpeg composite); the song, keyframes, and clips come from create-music-elevenlabs / create-image-fal / create-video-fal. Use for the song-driven-music-video format.From its SKILL.md
npx -y skills add gooseworks-ai/goose-skills --skill render-song-mvAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
SKILL.md
3.8 KB, 820 tokens by cl100k_base, as published. Nobody here has run it
render-song-mv
Assemble a song-driven music-video ad from a config: a purpose-written, sung song is
the entire narration (no separate voiceover), and every visual beat is timed to the lyrics.
The delivered song sets the timeline; N tableaux (one keyframe → one image-to-video clip per
lyric beat, all in a single look pack) are cut to their lyric windows and hard-concatenated
on the beat, captions are built from the song's OWN word timings with the hook line landing
on the chorus drop, and the spot closes on a PIL brand end card. It reads like a tiny animated
music video, not a demo. scripts/config.example.json is the worked example (Loóna "Fall In
Love With Sleep Again", 28s paper-craft 9:16); scripts/PIPELINE.md maps every config block
to its step and scripts/README.md documents the free assembly.
Run
This is the FREE, deterministic assembly stage — it spends nothing. The three paid
inputs are separate capabilities: the sung song (create-music-elevenlabs, music_v1,
force_instrumental FALSE — the lyrics ARE the script, returns mp3 + words.json), one
keyframe per tableau (create-image-fal), and one Kling 3.0 i2v clip per tableau
(create-video-fal). Given the delivered song + words.json + one clip per beat,
render-song-mv cuts each clip to its lyric window, hard-concats on the beat, builds the
lyric-synced captions, composites the PIL end card, and muxes → the master. Re-cuts reuse
the existing song / keyframes / clips and cost $0.
Contract (the free assembly)
- The sung song carries the narration — no separate VO. The generated ElevenLabs track
IS the bed and the script (
force_instrumentalfalse); do not add a spoken voiceover or a second music bed. - Plan the timeline AROUND the delivered song. The song is generated first and reshapes/
overshoots length; snap every tableau boundary to the lyric-phrase edges in the returned
word timings (
timeline.json) — never trim the song to a pre-planned grid. - Captions from the song's OWN word timings, not Whisper (script-window). Chunk
audio/words.json(~3 words at lyric boundaries); accent words get the warm-glow color. Whisper on sung audio returns "🎵 Music Playing 🎵", so it can't caption lyrics. - Land the hook on the chorus drop. Exactly ONE hero tableau (
is_hook) is timed so the payoff word (song.hook_word) sits on the chorus drop; accent that word in the captions. - One look pack for consistency. A single
style_opener+negative_tail+ palette drives every keyframe so N beats read as one film; no morph within a clip. - Hard cuts on the beat. Cut each clip to its lyric window and hard-concat — no dissolves (one optional match-cut into the hero reveal).
- PIL end card from the real app icon — never AI-render brand text. The lockup is composited deterministically (brand gradient + circular app icon + wordmark + tagline + CTA) from the brand's real asset; a diffusion model garbles a wordmark.
- FFmpeg composite, deterministic, FREE. Burn the caption ASS, overlay the end-card PNG on the final window, mux the song, boost the climax beat, loudnorm to −14 LUFS → 1080×1920 h264+aac. No paid calls, no keys.
What ships with it: 5 files
25.2 KB alongside SKILL.md
scripts/
- config.example.json15.7 KB
- PIPELINE.md4.8 KB
- README.md3.1 KB
tests/
- smoke-test.md1.2 KB
- skill.meta.json305 B