# Repurpose Long Form Content

> Turn a long video, podcast episode, talk, or livestream into short-form assets: clip candidates with timestamps, a newsletter issue, a thread, chapter markers, pull quotes, or a blog post. Use when the user says "repurpose this", "make clips from this video", "turn this podcast into a newsletter/thread/post", "find the best moments", or "chapters for this video". Covers getting a timestamped transcript first (the step everyone skips), choosing moments with evidence instead of vibes, cutting clips with ffmpeg, and the drafting rules that keep the output in the creator's voice.

- Skill: `franciscobmacedo/repurpose-long-form-content` (Agent Skill)
- Install (CLI): `npx skillmds@latest add franciscobmacedo/repurpose-long-form-content`
- Raw SKILL.md: https://api.skillmd.com/api/skills/franciscobmacedo/repurpose-long-form-content/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: franciscobmacedo (https://skillmd.com/u/franciscobmacedo)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/franciscobmacedo/repurpose-long-form-content

---


# Repurpose long-form content

Repurposing is a sourcing problem before it is a writing problem. A thread written from a vague memory of the video is generic; one written from a timestamped transcript with the exact lines quoted is specific. Get the material first.

## 1. Get the material (transcript with timestamps, plus comments)

You need three things: a **timestamped** transcript (SRT/VTT, or Whisper output), the **metadata** (title, description, chapters if the creator wrote them), and ideally the **comments** (what the audience already found quotable; the top comments are free editorial judgment).

- Local file (a recording the user owns): `whisper episode.mp3 --model small --output_format srt,txt` (or faster-whisper). Add `--word_timestamps True` if you'll cut tight clips.
- Public URL: use the `video-transcript` skill. Ask for the caption *file*, not just flattened text. yt-dlp: `--write-subs --write-auto-subs --sub-langs "en.*,en" --skip-download`. Comments: `--write-comments` (see `social-post-comments`).
- On a server / many episodes / bot-walled IP: the Post Reef API returns `transcript.txt`, timestamped `.srt` subtitle files, `comments.json` and metadata in one call (`--parts transcript,comments`; ~$0.008 per video; **paid, by the author of this skill**). If you also want the "moments" picked in the same call, give it a schema (§2 has one) with `--inputs transcript,comments`; that adds an AI charge per second of video. Videos over 60 minutes are rejected there; split long podcasts or use Whisper locally.

Keep the SRT. Clip timing comes from it; don't throw away the only source of time.

## 2. Find the moments with evidence

Don't ask a model "what are the best moments". Ask it for a typed list you can check against the transcript:

```json
{
  "type": "object",
  "properties": {
    "moments": {
      "type": "array",
      "items": {
        "type": "object",
        "properties": {
          "start": {"type": "string", "description": "HH:MM:SS where the self-contained moment begins (a sentence boundary, not mid-word)."},
          "end": {"type": "string", "description": "HH:MM:SS where it ends. 20–75 seconds for vertical clips; up to 3 minutes for a newsletter excerpt."},
          "hook": {"type": "string", "description": "The first line a viewer hears, verbatim from the transcript."},
          "why": {"type": "string", "description": "One sentence: what makes this standalone (a claim, a story with a payoff, a contrarian take, a number, a how-to)."},
          "kind": {"type": "string", "enum": ["claim", "story", "howto", "contrarian", "number", "funny", "emotional"]},
          "audience_signal": {"type": "string", "description": "Quote a comment that reacts to this moment, if any. Omit if none."}
        },
        "required": ["start", "end", "hook", "why", "kind"]
      },
      "description": "8–15 candidates ranked best first. Every timestamp must exist in the transcript."
    },
    "chapters": {"type": "array", "items": {"type": "object", "properties": {"start": {"type": "string"}, "title": {"type": "string", "description": "≤ 6 words, no clickbait"}}, "required": ["start", "title"]}},
    "one_line_summary": {"type": "string", "description": "What this episode is about, in the creator's register, ≤ 25 words."}
  },
  "required": ["moments", "chapters", "one_line_summary"]
}
```

Feed it the SRT (timestamps included) and the comments. Then **verify every `hook` string actually appears in the transcript** near `start`; drop any that don't. This one check removes most hallucinated moments.

Heuristics that hold up: moments that start with a claim or a number outperform ones that start with context; a story needs its payoff inside the clip; a "contrarian" take needs the reasoning inside the clip or it reads as rage-bait; comments that quote a line back are the strongest signal you have.

## 3. Cut the clips

```bash
# Re-encode so cuts are frame-accurate (stream copy snaps to keyframes and drifts by seconds)
ffmpeg -ss 00:12:41 -to 00:13:29 -i episode.mp4 -c:v libx264 -preset fast -crf 20 -c:a aac clip01.mp4

# Vertical 9:16 with a centered crop (for talking-head; for two-up layouts you need real editing)
ffmpeg -ss 00:12:41 -to 00:13:29 -i episode.mp4 -vf "crop=ih*9/16:ih,scale=1080:1920" -c:a aac clip01_vertical.mp4

# Burn captions from the SRT slice (extract the slice with the same timing first)
ffmpeg -i clip01.mp4 -vf "subtitles=clip01.srt:force_style='FontSize=18,Outline=1'" clip01_captioned.mp4
```

Add 0.5–1s of lead-in before the hook line so the first word isn't clipped. Name files by timestamp so they trace back.

## 4. Draft the written formats

Rules that keep it from sounding like every other repurposed post:

- **Quote, don't paraphrase, for the anchor line.** One verbatim line from the transcript per section; the rest can be your words.
- **Keep the creator's register.** Read 200 words of their description/transcript before drafting. If they say "y'all", don't write "individuals".
- **One idea per unit.** One tweet = one claim. One newsletter section = one moment from §2.
- **Cite timestamps** in the newsletter and blog post (`[12:41]`), linked to `?t=761` on YouTube. Readers click; creators love it.
- **Don't invent takeaways the creator didn't make.** If the model's "key lesson" isn't in the transcript, cut it.
- Thread: hook tweet is the best `hook` from §2 with its number/claim, then 5–8 tweets each anchored to a moment, last tweet links the source.
- Newsletter: `one_line_summary` as the subhead, 3–5 moments as sections with a quote + your 2-sentence gloss + timestamp, a "what the comments said" box if you have comments.
- Chapters: paste `chapters` as `MM:SS Title` lines into the description; YouTube requires the first at `0:00` and at least three, ≥10s apart.

## 5. Deliver

Hand over: `moments.json` (verified), the clip files, the drafts, and a one-paragraph note saying what source you worked from (manual captions / auto-captions / Whisper; comments fetched or not). If any moment was dropped in verification, say how many. If the transcript was auto-captions, warn that names and numbers in quotes need a listen before publishing.