Assemble an editorial-motion podcast-clip ad from a config — a real clipped podcast MP3 carries the narrative while N flat 2-tone editorial-illustration keyframes are animated NOT by generative i2v but by DETERMINISTIC ffmpeg ken-burns (zoompan) + hard cuts (no crossfades, which expose geometric drift), each beat snapped to its spoken line, the real audio muxed, Whisper-driven captions burned only mid-sentence, and closed on a PIL brand end card — never AI-rendered text. This is the FREE deterministic assembly stage (ffmpeg ken-burns + hard concat + audio mux + captions + end card); the real audio is clipped from source and the keyframes come from create-image-fal. Use for the editorial-motion-podcast format.
npx skills add https://github.com/gooseworks-ai/goose-skills --skill render-editorial-motion-podcast
Assemble an editorial-motion podcast-clip ad from a config: a real clipped podcast audio
line carries the whole narrative and every visual beat is timed to the sentence it describes,
in a bold flat 2-tone editorial-illustration look ("a New Yorker spot-illustration that
moves"). The motion is not generative video but deterministic ffmpeg ken-burns on static
keyframes, so it reads as a printed page that moves. This capability is that **FREE,
deterministic assembly** — the ffmpeg motion, hard-concat, audio mux, caption burn, and PIL
end card.
scripts/config.example.json is the worked example (Klarify "Rat Park", ~40.8s 1080×1920
9:16, 6 beats); 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 on the motion layer.
The paid inputs are separate: the real podcast MP3 is clipped from source (free ffmpeg) with
its Whisper word timings, and one editorial-illustration keyframe per beat (chained ref images
so cage/character geometry holds) comes from create-image-fal (Nano Banana). Given the
clipped audio + words.json + the per-beat keyframes + the real brand wordmark PNG,
render-editorial-motion-podcast renders each keyframe as a ken-burns segment, hard-concats
on the beat, muxes the real audio, burns the mid-sentence captions, and composites the PIL end
card → the master. Re-cuts reuse the existing audio / keyframes and cost $0.
narration MP3 (-map 0:v:0 -map 1:a:0) — a real clipped podcast line (preferred) OR an
approved generated VO (create-vo-elevenlabs). Never a sung/generated track. (Clip-vs-generate
is the recipe's STEP-0 intake decision — if no source episode is supplied, ASK the user.)
with zoompan (push-in / pull-back, 1.0→~1.06×, 24fps); Seedance/Kling are photoreal-trained
and invent naturalistic middle states that collapse the 2-tone look. Never -loop 1 with
zoompan d=N (it balloons the duration); feed a single image and clamp with -t + trim.
other; hard-concat each beat's segments and split long beats into micro-cuts (target 8–10
distinct visual moments). Each beat's visual STARTS within ~0.5s of its spoken line.
frosted-subtlecaptions while the speaker talks; leave silent/reflective beats and the end card uncaptioned.
THREE mandatory rules (each bit us in prod — bake them in):
(end = min(last_word_end + ~0.15, next_start - 0.03)). Two boxes must never stack at the
same spot; an end-tail bleeding into the next window is the #1 caption bug.
upper ~75% (see the recipe's look_pack.caption_safe_area). If a finished keyframe's subject
intrudes into the caption band, deterministically shift the subject UP into the empty top
space (PIL: paste up ~0.24H onto a canvas pre-filled with the exact paper color from a clean
corner) — never let the box sit on the subject.
ass/subtitles filter), but **check ffmpeg -filtersfirst**: many builds (Homebrew) lack libass/drawtext. If absent, use the deterministic
overlay fallback — render each line as a transparent PNG (frosted rounded box + white
text, PIL) and composite via the ffmpeg overlay filter with timed
enable='between(t,st,en)' windows. Same look, no libass.
composited deterministically (stretched-gradient bg + feathered mascot crop + wordmark +
tagline with a system font); a diffusion model garbles a wordmark ("therapits"). The video
runs a ~1.5s silent hold past the audio on the end card (fade first/last 0.3s).
audio, burn the captions, hold on the end card → a 1080×1920 h264+aac master. No paid calls.
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Take gooseworks-ai/render-editorial-motion-podcast from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.