wjs-segmenting-video

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---
name: wjs-segmenting-video
description: Use when the user has a long-form video (interview / lecture / podcast / conversation) and a transcript SRT, and wants to extract 3–6 stand-alone topical short clips from it. This skill ONLY cuts and crops — it produces raw clips + per-clip SRTs as a hand-off package for downstream post-production (`/wjs-overlaying-video`). Triggers — "切成几段", "分主题", "拆成短视频", "切片", "topic segments", "split into clips".
---

# wjs-segmenting-video

Cut a long video + SRT into multiple stand-alone short clips, each
oriented for the target platform. **This skill stops after cutting +
cropping** — it hands off the raw clips to `/wjs-overlaying-video` for
covers, captions, illustrations, CTA, and final render.

## When to use

- Long-form video (≥10 min) with an existing SRT transcript.
- Goal is **stand-alone** short clips (each viewable without context).
- The user will (or you will) drive post-production separately in
  `/wjs-overlaying-video`.

## When NOT to use

- Single-topic trimming → just use `ffmpeg -ss A -to B`.
- No transcript yet → run **`/wjs-transcribing-audio`** first (then `/wjs-translating-subtitles` if the segments need a non-source language).
- Multicam editing → use **`/wjs-editing-multicam`**.
- Highlight reel with multiple cuts inside a single topic → that's
  editing, not segmentation.

## What this skill IS — and IS NOT

| Is | Is not |
|---|---|
| You (the agent) **read the full SRT and decide the topic boundaries** | A script that runs NLP topic modeling, silence detection, or "viral moment" scoring. Topic boundaries are semantic; competing tools (Descript, OpusClip, Riverside Magic Clips) all get this wrong by automating it. |
| `segment.py` cuts; `/wjs-reframing-video` reorients | An end-to-end "magic" pipeline |
| Accurate-seek cuts by default (re-encode) — clip starts EXACTLY at requested timestamp | Stream-copy cuts (those produce keyframe-snap drift up to GOP duration) |
| Hands off **raw cropped clips + per-clip SRTs** | Burned subtitles, covers, intros, CTAs (those live in `/wjs-overlaying-video`) |

## The pipeline

```
long video + SRT
   ↓     (agent reads SRT, decides topics — judgment, not parsing)
segments.json
   ↓     segment.py --reencode (accurate seek; clip starts exactly at requested t)
clip_NN.mp4 + frame_NN.jpg
   ↓     ASK: target platform orientation match source?
   ↓     /wjs-reframing-video on each clip (if 16:9 → 9:16, etc.)
   ↓     re-extract frames from cropped clips
clip_NN.mp4 (now in target orientation) + clip_NN.zh-CN.burn.srt
   ↓
HAND OFF → /wjs-overlaying-video
   (does covers + captions + illustrations + CTA + final render)
```

## Step 1 — Read SRT, write `segments.json`

**Don't outsource topic identification to a script.** For each candidate segment, judge:

- **Self-contained?** A cold viewer must understand it without prior context.
- **Single thread?** One central question / insight; if the speaker pivots mid-clip, that's two segments.
- **Length fits platform?** 60–180s for 视频号 / 30–60s for 抖音&Shorts. <30s feels truncated; >4min loses retention.
- **Hook + payoff?** Open on a claim / question / vivid image; close on a takeaway. Never end mid-sentence.
- **Snap to SRT cue boundaries** — never cut mid-word.

3–6 strong segments from a 10-minute source is normal. Drop boring middles. Quality > quantity.

Schema (full spec in `references/segments_schema.json`, example in `references/example_segments.json`):

```json
{
  "source_video": "input.mp4",
  "source_srt": "input.zh-CN.srt",
  "platform": "wechat_channels",
  "segments": [{
    "id": 1, "slug": "intent-not-code",
    "title": "AI 时代不是写代码\n而是写意图",
    "summary": "Two-sentence pitch — what's the insight, what's at stake.",
    "start": "00:00:43.460", "end": "00:02:35.220",
    "cover_prompt": "Visual concept for gpt-image-2 (style anchor, not literal scene)"
  }]
}
```

`slug` = kebab-case English (used in filenames). `title` uses `\n` for line break, 2 lines max, 8–12 Chinese cha