Edit style: dynamic vertical reels from a raw take

You turn one raw talking-head clip into a finished 1080x1920 reel that looks hand-edited: the frame never sits still, graphics are real UI or official logos (never stock, never invented), and captions land on the beat. Nothing is dragged on a timeline. Every decision is a written shot plan rendered by code, then checked by gates that measure the final file.

Part of a pipeline: reel-ideation (ideas, scripts, record sheet) -> footage-ingest ("the card is in": copy, sort, pick takes, conform) -> edit-style (short-form edit) or longform-edit (YouTube long-form) -> reel-captions (post text) -> trial-post (trial reels) -> reel-scorecard (72 h read).

Requirements

Claude Code on a computer (not Claude.ai: it needs a shell and ffmpeg).

Install check: sh tools/check_setup.sh (exit 0 = ready).

Inputs

• raw.mov (or mp4): one take, phone or camera, any orientation.
• script.txt: what was said, spelled the way it must appear on screen. If there is no script yet, write one first with references/script-writing.md.
• Optional: a topic URL (repo, product page) to capture, and sources for every number.
• From footage-ingest: a SHIP line (hook clip > body clips > IG ending / bio ending). Clips recorded one line per clip are joined in step 2; each join is a place dead air hides.

Pipeline (one folder per reel: every file for this reel lives inside it)

transcribe.py below is use-local-whisper/scripts/transcribe.py. Parallel edits never share a work folder: one reel, one folder.

0. Conform camera footage first (footage-ingest/scripts/conform.py): constant 30 fps, TV range, and the mirror if the creator wants selfie view. The mirror goes on the raw camera clip only, BEFORE captions, logos, cards or screen captures are composited; those are never mirrored. Check the mirrored frame for backwards text.
1. Check the source. ffprobe -v error -select_streams v:0 -show_entries stream=color_range,color_transfer,r_frame_rate -of csv=p=0 raw.mov arib-std-b67 = iPhone HDR, tone map first; pc,bt709 = full-range camera file. Exact handling: references/source-footage.md.
2. Cut retakes, then pauses. Transcribe raw (transcribe.py raw.mov), keep the LAST take of any repeated line, never cut mid-word. Then python3 tools/tighten.py cut.mp4 tight.mp4 (-50 dB, never -38 dB). Joining one-line clips: set each in and out point within 0.08 s of the first and last word (refine the edge on the audio RMS, not on Whisper), look at the first and last 0.5 s of every clip (no reaching for the camera, no settling in), and write the join times of the final timeline to joins.json.
3. Transcribe the tightened take: transcribe.py tight.mp4 gives tight.words.json.
4. Write the shot plan (shots.json): one row per shot, {layout, anchor_phrase, content}. anchor_phrase is script text matched against words.json so the shot starts when those words are said. Never hand-type timestamps. Layout rules: references/edit-craft.md.
5. Capture and fetch assets. Screens: node tools/capture_clock.js <url> shots/s3 6 (headless, frame-exact). Logos: the brand's official press page or site SVG only. Open every asset and look at it before use.
6. Frame the face per take: python3 tools/face_box.py tight.mp4 --model yunet.onnx --solve full (and --solve pip). Never reuse crops from another take.
7. Render panels (Remotion or stills) to each shot's exact duration, composite per shot with ffmpeg, concat, lay the continuous voice on top. Run renders under nice -n 15. Look-dev on stills, then one full render.
8. Mix and master (references/edit-craft.md section 9): high-pass 75 Hz, light denoise, synthesised SFX 12 to 20 dB under the voice, -14 LUFS, true peak at or under -1.5 dBTP.
9. Captions, timed on the FINAL audio: transcribe.py final_nocaps.mp4, then python3 tools/make_cues.py script.txt final_nocaps.words.json --fix fix.json, then python3 tools/burn_captions.py final_nocaps.mp4 cues.json final.mp4 --caps. Text comes from the script, timing from Whisper. --y 0.38 on PIP shots.
10. Gates (below). Fix and re-run until all pass.

Layouts

Switch every 2 to 5 s. Open on the face from frame 1 (FULL or PIP): never a graphic, title card, screen recording or logo-only opener; openers without a face test badly. Break to GFX on each key beat, land the CTA on GFX, close on FULL. If the topic is a named brand, show its official logo in the first 2 s as a clean panel beside or over the face, never instead of it.

Two endings per keyword reel

Comment-keyword DM automations usually work on Instagram only, so a "comment KEYWORD" ending wastes TikTok, YouTube and Facebook traffic. Export two files:

• final.mp4: Instagram, ends on the recorded "comment KEYWORD" clip.
• final-bio.mp4: everywhere else, ends on a recorded "check the link in my bio" clip.

Make the swap invisible: hide the join under a full-screen GFX card, use one continuous bio clip (no micro-cuts), match scale, colour and loudness, 60 to 100 ms audio crossfade, and frame-check the join. Both files go through every gate.

QA gates (all must pass; write results to QA.md)

Run gates 10 to 13 on final.mp4 AND final-bio.mp4, each with its own joins file.

On a caption-sync fail: retime that chapter's cues to the final words, re-burn, re-transcribe, re-gate. Loop until every chapter passes.

Output

reel-folder/
  final.mp4        1080x1920, H.264 BT.709, AAC 48 kHz, -14 LUFS (Instagram ending)
  final-bio.mp4    same edit with the "link in my bio" ending (every other platform)
  joins.json       join times per file (joins-bio.json for final-bio.mp4)
  captions.srt     from the re-transcription of final.mp4, names spelled right
  contact.png      12-frame sheet you looked at
  QA.md            one line per gate, plus CLAIMS and any cuts of private detail
  LOG.md           written as you go

Example QA.md:

framing      12/12 PASS (head-top min 6.1%, chin max 88.4%)
face first   9/9 frames PASS   dead air max 0.21 s at joins PASS   join envelope PASS   dashes PASS
caption sync hook median 0.04 p95 0.11 PASS | body 0.05/0.14 PASS | cta 0.03/0.09 PASS
A/V          diff 0.012s PASS     loudness -14.1 LUFS, -1.8 dBTP PASS
colour       yuv420p tv bt709 13.9 Mbps PASS   skin luma diff 1.2 PASS
contact      looked: no pills, captions clear of mouth, logos official, 4:5 safe
CLAIMS       "40,000 stars": GitHub API 14:05 today = 41,212 (shown as 41K)

Failure handling

• No Whisper: stop and install one (use-local-whisper). Never guess timings.
• No OpenCV: skip gate 1's tool but eyeball every contact-sheet frame for chin/forehead cuts, and say so in QA.md.
• No Remotion: build GFX shots as still PNGs (Pillow or a headless Playwright screenshot of an HTML card) with a slow push-in (zoompan).
• make_cues.py exits 2 (script and audio differ): fix the script to match what was said. Never burn captions that disagree with the voice.
• A gate fails three loops in a row: stop, report the numbers, ask.

Rules that keep it from looking like AI

• No floating pills, badges, corner tags, stickers or REC chips. Ever.
• No code-generated motion-graphics sizzle. Real footage and real product screens.
• One accent colour. One caption style per shot type.
• Show, don't narrate: when the voice names a thing, that thing is on screen.
• Nothing private on screen: finances, client names, account ids, internal files.
• No sponsor text, "paid partnership" label or disclosure card burned into the video: disclosure goes in the post caption and the platform's paid-partnership toggle.
• No "AI clone" or "digital twin" label burned in on avatar shots: use the platform's AI-content toggle.
• No em or en dashes in scripts or captions.

Depth: references/script-writing.md (hooks, register, numbers), references/source-footage.md (HDR, full-range cameras, clone takes, silence), references/edit-craft.md (captions, screens, framing, gestures, audio, pitfalls).