| name | video-cut |
| description | End-to-end turn an unedited long-form talking-head / vlog / podcast video into a compact "first cut" (rough cut). Use when asked to edit/剪辑 a raw YouTube (or local) video into a tighter version: download, word-level transcribe, diagnose bad-edit spots (slow intro, fillers, dead air, tangents, rambling), decide cuts in a JSON edit plan (each kept/dropped span with in/out + a one-line reason), render with ffmpeg, then self-check the result (re-transcribe + frame/black/ silence checks). Triggers: "剪成第一版", "rough cut", "first cut", "压缩时长", "把这条原片剪短", "cut down this video".
|
Video Cut
Turn one unedited long video into a compact, watchable first cut — decisive,
content-aware, and self-verified. Human (you, the model) makes the editorial
decision of WHAT to keep; the bundled scripts do the mechanical precision (boundary
alignment, dead-air reclaim, memory-safe render, self-check).
When to use
- A raw/unedited video (YouTube URL or local file) needs to become a tighter cut.
- The ask is "halve it", "cut the boring parts", "make a first/rough cut".
- NOT for: word-by-word filler micro-surgery, multi-cam B-roll assembly, color/audio
polish — those are a later P1/P2 polish pass. This skill is P0: validate the
diagnosis + produce a solid cut.
Dependencies
/video-understand is a prerequisite. Run it first and use its validated media,
transcript, analysis, and timeline instead of recreating them in this skill.
Before starting, verify that it is installed. If it is not, warn the user that
this prerequisite is missing and stop before processing media.
Activate any environment that has these (do NOT assume a specific conda env name):
yt-dlp (download), ffmpeg/ffprobe (cut/render/probe) on PATH- Python with
faster-whisper (CPU works: device=cpu, compute_type=int8)
Check first: yt-dlp --version, ffmpeg -version, python -c "import faster_whisper".
Project protocol workflow
Use these durable project files:
work/cut/edit-plan.json # hand-authored keep/drop decisions only
work/timeline.json # generated precision ranges + source/program mapping
review/01-cut/cut-summary.md
review/01-cut/timeline-map.png
review/01-cut/boundary-review.mp4
review/01-cut/full-proxy.mp4 # optional whole-program pacing review
Each canonical decision has a stable id, action (keep or drop), start_s,
end_s, reason, and semantic/transcript evidence_refs. Keep decisions chronological;
V1 does not support reordered or duplicated source clips.
Reuse the shared evidence layer rather than transcribing or diagnosing again:
python skills/video-understand/scripts/transcribe.py work/cache/audio16k.wav work/understand/transcript
python skills/video-understand/scripts/analyze.py work/understand/transcript.json work/understand/analysis.json
The existing precision scripts accept the canonical plan. Expand word-safe boundaries,
optionally assign linear varispeed, then generate the shared timeline:
python skills/video-cut/scripts/build_edit.py work/cut/edit-plan.json work/understand/transcript.json work/cache/edit-final.json
python skills/video-cut/scripts/assign_speed.py work/cache/edit-final.json work/understand/transcript.json
python skills/video-understand/scripts/build_timeline.py work/cache/edit-final.json work/timeline.json --fps-num SOURCE_FPS_NUM --fps-den SOURCE_FPS_DEN
Use the exact rational FPS from work/understand/media.json; never round 30000/1001 to
30/1. Review timeline-map.png and the short boundary-review.mp4. Generate
full-proxy.mp4 only when whole-program pacing must be reviewed. Write the result to
cut-summary.md and point the operation check report at that file.
Record the timeline contribution with its required source input, update the operation integer
revision, and set based_on to the exact understanding revision consumed:
"render": {
"kind": "timeline-transform",
"input": "../input/original-video.mp4",
"plan": "timeline.json"
}
The shared delivery renderer encodes the active sequence after all revision checks pass.
Standalone compatibility workflow
The legacy files and commands below remain supported during migration. build_edit.py
accepts both edit_coarse.json and canonical decisions[]; cut_render.py accepts both
edit_final.json and timeline.json; this skill's transcribe.py is a compatibility
wrapper around ../video-understand/scripts/transcribe.py.
Keep all intermediates under work/, deliver to project root:
work/source.mp4 # downloaded original
work/audio16k.wav # 16k mono audio for whisper
work/transcript.json/.srt # word-level timestamps
work/analysis.json # diagnosis (silences/fillers/runs)
work/edit_coarse.json # *** YOUR editorial decision (hand-written) ***
work/edit_final.json # expanded tight render segments (generated; carries per-segment speed)
work/selfcheck/ # cut re-transcript + join frames
first_cut.mp4 # THE DELIVERABLE
first_cut_report.md # edit + self-check report
The pipeline (6 steps)
1. Download
Highest accessible quality + audio. The default player client often fails
(not-available / DRM / sign-in). The reliable fallback is the android client:
yt-dlp --extractor-args "youtube:player_client=android" \
-o work/source.mp4 "<URL>"
If only a muxed 360p (format 18) is reachable without login, accept it and document
the constraint — HD DASH is frequently DRM/login-gated. Then extract audio:
ffmpeg -y -i work/source.mp4 -ac 1 -ar 16000 work/audio16k.wav
2. Transcribe (word-level)
python scripts/transcribe.py work/audio16k.wav work/transcript [base.en]
Produces work/transcript.json (segments + per-word start/end) and .srt.
faster-whisper, CPU/int8, VAD on, word_timestamps=True.
Non-English: pass a multilingual model + --lang, e.g.
python scripts/transcribe.py work/audio16k.wav work/transcript medium --lang zh
(the .en models are English-only). Verify the language on a short sample first.
Note: Whisper strips most vocalized "um/uh" — they are NOT transcript-measurable,
so filler detection must combine the lexical word-search below with silence-gap
analysis, not rely on the transcript text alone.
3. Diagnose ("see" + "read")
python scripts/analyze.py work/transcript.json work/analysis.json
Surfaces: leading/trailing dead air, inter-word silences ≥0.8s (with the word they
follow), filler counts, wpm, speaking_ratio, near-duplicate repetitions, monologue
runs >40s (ramble candidates).
To see the picture, sample a frame every ~2s into a contact sheet and read it
yourself; confirm scene structure / hunt black or junk frames:
ffmpeg -y -i work/source.mp4 -vf "fps=1/2,scale=240:-1,tile=10x10" -frames:v 1 work/contact.png
ffmpeg -i work/source.mp4 -vf "blackdetect=d=0.1:pic_th=0.98" -an -f null -
For a single static talking-head shot there are no scene cuts — expect this, and
note that the later jump-cuts are normal, not glitches.
4. Decide the cut — write edit_coarse.json BY HAND
This is the editorial core. Read the transcript + analysis, then write a coarse plan
of keep-blocks and drop-spans that tile the entire timeline with no gaps/overlaps,
each with in, out, and a one-line reason. Compress slow preamble to a few
high-value nuggets; keep substance as large blocks; drop clear tangents/rambles.
Preserve chronological order; the canonical V1 timeline rejects reordering.
Aim to roughly halve runtime.
5. Expand → auto-speed → render
Turn coarse blocks into tight, dead-air-free render segments, normalize the speaking
pace, then render:
python scripts/build_edit.py work/edit_coarse.json work/transcript.json work/edit_final.json 1.5
python scripts/assign_speed.py work/edit_final.json work/transcript.json # adds per-segment speed
python scripts/cut_render.py work/edit_final.json work/source.mp4 first_cut.mp4
build_edit.py splits each block at internal pauses > SPLIT_GAP (4th arg, e.g. 1.5s)
and trims every boundary INTO the silence (LEAD 0.10s / TRAIL 0.30s) so no word is
clipped and dead air is reclaimed. Lower SPLIT_GAP = tighter but choppier; for a
slow, deliberate speaker prefer ~1.5, not 0.6 (avoids twitchy micro-cuts).assign_speed.py (auto varispeed) — groups render segments by decision_ref, measures
the speaking pace of each hand-authored keep decision, and writes one shared speed
factor plus each segment's resulting out_dur back into edit_final.json. Pace already
inside the comfortable band stays at 1.0; out-of-band pace moves only to the nearest
deadband edge. Adjacent decision blocks differ by at most 0.08x to avoid "pumpy" joins.
It changes pace only — never which content is kept. Read the printed table before rendering:
- Metric: words/min (English) — auto-switches to chars/min for CJK (zh/ja/ko).
- Defaults are first-run starting points, not validated constants: deadband 145–185
WPM and speed clamped to 1.0–1.3. This is speed-up-only: slow decision blocks can
be tightened, while natural-fast delivery remains at 1.0. Short decision blocks
(<3.5s / <6 words) are too noisy to measure and inherit the global factor.
- The rate is measured after dead-air reclaim, so it reads higher than gross WPM and is
closer to articulation rate. The printed-table review is load-bearing: eyeball it
and tune
deadband to the speaker before rendering, don't trust the defaults blind.
- Knobs (k=v):
mode=segment|global|off (default segment; global=one factor for
the whole video, off=disable), deadband=lo,hi, min_speed=/max_speed=,
min_seg=/min_words=, lang=auto|en|cjk. To skip the feature entirely, just don't
run this step — cut_render.py treats a missing speed as 1.0.
cut_render.py uses N separate seeked inputs + concat filter, single re-encode.
This is mandatory: trimming N times off ONE decoded input makes ffmpeg buffer the
whole decoded stream (tens of GB for a long video). See reference/pitfalls.md.
When segments carry a speed, it re-times each input (setpts video / atempo audio)
and normalizes fps/timebase/sample-rate per input before concat so A/V stays locked;
with no speed it falls back to the exact original graph.- Final comparison:
video-edit-compare reads the canonical timeline plus the actual
final delivery. It projects final pixels back to source time, stretches varispeed clips
to their source duration, and shows dropped ranges as black.
6. Self-check — do not declare done until this passes
# protocol precision regression check (run from the repository root)
python skills/video-cut/scripts/check_project_protocol.py
# re-transcribe the finished cut
ffmpeg -y -i first_cut.mp4 -ac 1 -ar 16000 work/selfcheck/cut_audio16k.wav
python scripts/transcribe.py work/selfcheck/cut_audio16k.wav work/selfcheck/cut_transcript
# content + filler + boundary check
python scripts/verify_cut.py work/edit_final.json work/transcript.json work/selfcheck/cut_transcript.json
# frame / black / join check (join times use post-speed out_dur, so they stay accurate)
python scripts/selfcheck_frames.py work/edit_final.json first_cut.mp4 work/selfcheck
# A/V sync + no residual dead air
ffprobe -v error -select_streams v:0 -show_entries stream=duration -of csv=p=0 first_cut.mp4
ffprobe -v error -select_streams a:0 -show_entries stream=duration -of csv=p=0 first_cut.mp4
ffmpeg -i first_cut.mp4 -af silencedetect=noise=-32dB:d=1.5 -f null -
Pass thresholds:
| Check | Pass condition |
|---|
| Black frames | none |
| Join frames | clean — only natural head-jumps, no black/frozen/torn |
| A/V sync | video vs audio duration within ~1 frame (≈30ms); total ≈ final_duration_s_after_speed when varispeed is on |
| Dead air ≥1.5s | 0 (speaking_ratio should rise markedly) |
| Dropped/dup segment | none — all key content present (difflib ~0.90 is OK; lower |
| ratio is usually 2nd-pass transcription noise on mumbled speech, not loss — verify | |
| by keyword presence before blaming the cut) | |
| Cut-off half-sentences | none — boundaries land in silence / on sentence ends |
Honest filler caveat: paragraph-level cutting will NOT achieve "almost no fillers".
Per-minute density can even rise because the retained substance is conversational.
Report this honestly; word-level filler excision belongs to P1/P2 (and would make a
single-shot talking head visibly choppy).
Finally
Write first_cut_report.md: source summary, what was cut & why, the self-check table,
and any honest caveats (quality ceiling from 360p source, filler density, etc.).
If varispeed was applied, disclose it: how many segments were re-timed and the speed
range (from assign_speed.py's table / speed_params), so the reader knows the cut is
not 1:1 real-time. Note too that verify_cut.py's filler-per-minute figure reads slightly
higher after speed-up (same words, shorter minutes) — a reporting artifact, not added fillers.
Key principles (the hard-won ones)
- You decide content; scripts decide frames. Hand-write
edit_coarse.json;
let build_edit.py do precision and assign_speed.py do pace.
- Speed touches pace, never content. Auto-varispeed gives all render segments from
one
decision_ref the same speed, moves out-of-band pace only to the nearest deadband
edge, limits adjacent decisions to a 0.08x delta, and clamps output to 1.0–1.3.
atempo preserves pitch. Review the table; mode=off or skip the step to disable.
- Never clip a word. Boundaries always fall inside silence.
- Memory-safe render only (N seeked inputs + concat). Never multi-trim one input.
- Be decisive. Make reasonable calls and proceed; don't over-ask.
- Self-check is non-negotiable. Re-transcribe + frame-check before claiming done.
- See
reference/pitfalls.md for the full list of traps and fixes.
