shorts-from-takes

name

shorts-from-takes

description

Assemble several raw clips — multiple takes, separate shots, or existing reels — into ONE finished, on-brand vertical Short (1080x1920), PLUS ready-to-post YouTube / Instagram / TikTok / X metadata. Use this whenever someone hands over a batch of clips and wants them stitched into a single short-form video: it selects and orders the best material, fits each clip to vertical, burns word-by-word captions, adds a title card, and normalizes audio. Works for ANY content — talking-head, screen recordings, b-roll, montage. Trigger on "make a short/reel from these clips", "combine/stitch these videos into one short", "cut a vertical video from my takes", "edit these into a Short/Reel/TikTok", or any folder of clips to post as a Short. Opinionated layer ON TOP OF `video-use` — use video-use for a freeform single-video edit; cede to `video-to-articles` for a written post/article and `linkedin-carousel` for a slide/PDF carousel.

Shorts From Takes

Combine several raw clips — alternate takes, separate shots, or existing reels — into one finished vertical Short in a consistent house style, then generate platform-tuned posting metadata. This skill is a thin preset over video-use: video-use is the engine (transcription, timeline inspection, loudnorm, ffmpeg know-how); this skill carries the recipe — how to select and order the clips, the look, the caption style, the render script, and the metadata playbook.

When this applies

Trigger and content coverage are in the description above. The one routing rule worth repeating: for a general/freeform edit of a single video, use video-use directly — this skill is specifically for combining several clips into one posted Short.

Prerequisites

• ffmpeg + ffprobe on PATH, and Python 3 with pillow. Zero-setup run: uv run --with pillow python scripts/build.py <spec.json> (no venv to manage). Or pip install -r requirements.txt in any env.
• Transcription (word-level timestamps) — captions need one transcript JSON per clip:
  • Recommended: the video-use skill (engine) — clone https://github.com/browser-use/video-use, put an ELEVENLABS_API_KEY in its .env (verify current free-tier terms), then run its transcribe_batch.py + pack_transcripts.py. build.py auto-detects it; or point via the VIDEO_USE_HELPERS env var / video_use_helpers in the spec.
  • Bring your own: any tool that emits word timestamps (WhisperX, faster-whisper, Deepgram…) works — just match references/transcript-schema.md. No video-use needed.
• Loudnorm borrows video-use's helper if present, else a built-in 2-pass runs — so rendering never requires video-use; only transcription does (or a BYO transcript).
• Fonts are bundled in assets/fonts/ (Anton + Montserrat). Nothing to download.

The house style (defaults — deviate when the material calls for it)

• Format: vertical 1080x1920, 30fps, downscaled from source at near-lossless CRF.
• Structure: open on the strongest hook, end on the payoff/CTA, keep only what earns its place. A talking-head + demo piece might run HOOK → SETUP → SHOW → PROOF → PAYOFF → CTA; a montage might just be your best shots in rhythm. The beat labels are yours — the arc is whatever the material wants.
• Fit to vertical: portrait clips fill the frame (center-crop); wide clips default to a blurred fill so nothing is lost, or fit: cover to crop them full-frame. Orientation is auto-detected per clip (or set kind explicitly).
• Speed: native (1.0). Bump to ~1.2x (pitch preserved) to tighten talking-head takes.
• Look: native colour by default. Opt a clip into a warm relight with grade: true (lift shadows, tame highlights) — good for faces/skin; leave screen/UI and already-graded footage native.
• Transitions: hard cuts within a run of same-orientation clips; a quick 0.2s crossfade at orientation flips and between two different takes/shots you want to dissolve.
• Captions (optional): Hormozi style — Anton, ALL CAPS, thick stroke, word-by-word yellow fill, quick pop-in; in the cross-platform safe zone. Clips with no speech (or no transcript) simply carry none — montages work fine.
• Title card: top, first ~3s, fading out.
• Audio: gentle denoise + high-pass, normalized to −14 LUFS (turn denoise off for music-led clips).
• Output: versioned files (final_v1.mp4, final_v2.mp4, …) so iterations compare.

These are starting points, not laws. The creative picks — which clip, exact trim points, how warm, font size — are judgment calls made by looking at the footage.

Workflow

1. Inventory + transcribe (engine = video-use)

• ffprobe each clip (orientation, fps, duration). You can set kind per clip, or let the renderer auto-detect portrait vs landscape.
• If the clips have speech you want captioned, transcribe them (video-use's helpers, or any word-timestamp tool → references/transcript-schema.md): uv run python <video-use>/helpers/transcribe_batch.py "<folder>" --edit-dir "<folder>/edit" then pack_transcripts.py --edit-dir "<folder>/edit". Cache — never re-transcribe. Skip this entirely for no-speech montages.
• Read takes_packed.md for the words, AND scan a few frames per take (video-use's timeline_view.py) for the visual read — a transcript can't show look-aways or energy. Rank takes per beat: a fatal error (a mis-spoken product name / CTA) disqualifies first; among the clean takes pick the highest energy/delivery; break ties on the cleaner cut point.
• Loudness-scan every candidate span before the first render (ffmpeg -ss <start> -t <dur> -af loudnorm=print_format=json -f null -). Takes recorded without the mic in hand (both hands on a prop, filming a screen) run 15–20 LU under the rest, and global loudnorm can't fix imbalance between segments. Level them with per-seg gain (pure dB — voice character untouched). Found after render one, this costs a full re-render; found here, it's one spec field.

2. Select + order the clips + confirm the plan (judgment — do NOT skip)

• Choose the best clip/take per beat and order them so they flow. Drill into specific moments with video-use's timeline_view.py (filmstrip+waveform) to find clean cut points and to spot look-aways/awkward pauses to trim.
• For spoken clips, snap cuts to word boundaries and pad edges — the exact rule (and why cutting inside a word fails) is references/render-notes.md hard rule 8.
• Confirm the plain-English plan (clip order, trims, title, any name corrections) with the user before rendering.

3. Write the spec + render

• Copy scripts/spec.example.json, fill segments (src/start/end, optional kind/grade/fit/beat, per-seg gain for mic-less takes, per-seg zoom [z0,z1] for animated push-ins/pull-backs), blocks (group consecutive same-orientation clips; a crossfade fires between blocks), title, name_fix, version. Optional extras: highlight_words + captions.highlight (tint key words), top_titles (beat labels at the top safe zone), inserts (image B-roll glimpses). All documented in references/render-notes.md.
• Preview: python scripts/build.py <spec.json> --preview
• Final: python scripts/build.py <spec.json>
• See references/render-notes.md for the spec schema, the production-correctness rules, and the gotchas that cause silent failures (read it before changing the render).

4. Self-eval before showing the user

Render failures here are silent — a mis-ordered filter, a hidden caption, or a skipped loudnorm looks fine in the ffmpeg log and only shows on an actual frame or waveform. So grade the rendered file, not the plan, before the user ever sees it.

• Run the automated gate first: python scripts/build.py <spec.json> --check asserts output duration and −14 LUFS loudness against the spec (and reports the caption-cue count).
• Then eyeball what a script can't, per the Self-eval recipe in references/render-notes.md (frames at each cut, title, first/last 2s; captions readable + in safe zone; grade consistent; names corrected). Fix → re-render → re-check, cap 3 passes. Bump version each iteration so the user can compare.

5. Posting metadata (optional — when asked, or offer it)

Write platform-tuned metadata to <edit>/social_metadata.md per references/social-metadata.md — YouTube Shorts, Instagram Reels, X native video, and TikTok (the "when asked" fourth platform), each to that platform's CURRENT algorithm. Research current trends first (web search); the reference encodes the durable rules but trends move.

Files

• scripts/build.py — the render engine (reads a spec.json; --preview and --check modes). Self-contained: borrows video-use's loudnorm if present, else uses a built-in 2-pass.
• scripts/spec.example.json — worked example: several clips assembled into one Short (copy + edit paths).
• references/render-notes.md — spec schema + hard rules + gotchas. Read before editing render.
• references/social-metadata.md — the 4-platform metadata playbook + paste-ready templates.
• references/transcript-schema.md — the transcript JSON captions read (bring your own).
• requirements.txt — Python deps (pillow); ffmpeg/ffprobe are system tools.
• assets/fonts/ — Anton (caption/title), Montserrat (fallback); SIL OFL, licenses in assets/font-licenses/.

Staying current with video-use

This skill deliberately does NOT copy video-use's engine — it calls it, so upstream fixes flow through. Periodically git -C <video-use> pull to stay on the latest. If you extend the render in ways video-use lacks (mixed-orientation canvas, xfade chain, ASS karaoke), consider upstreaming them as a PR so the engine carries them and this skill shrinks.