AI prompts for repurposing one video into a content week

Turning one recording into a distribution plan

Here is a transcript with timestamps: [PASTE].

Produce a repurposing map: 6 vertical clips (start/end timestamps, standalone justification, 8-word hook), 3 carousel or thread outlines, 1 newsletter section, 5 quote graphics, and 3 title options for the full video.

Rule: every clip must make sense to someone who has not seen the rest. Reject anything needing setup and say why.

In practice: Feed in a 45-minute interview and get two weeks of posts.

Ranking moments objectively

Score each of these transcript segments 1-10 on: standalone comprehensibility, emotional or surprise value, quotability, and visual interest. Show the four scores plus a total.

Then list only the segments scoring 28 or above, and for each give the exact in/out timestamps and the strongest 8-word overlay hook.

Segments: [PASTE]

In practice: The scoring rubric keeps you from clipping the moment you personally liked most.

Text distribution that is not just a transcript dump

Turn this transcript into a [WORDS]-word article. Do not transcribe — restructure.

Rules: lead with the strongest insight even if it appeared late, use headings that state claims rather than topics, cut all verbal filler and tangents, keep any specific numbers or examples, and add a short intro that works for a reader who never watched.

Flag anything that only works spoken and should be dropped.

Transcript: [PASTE]

In practice: Restructuring rather than transcribing is what makes the article rank on its own.

Same insight, four formats

Take this insight from my video: [PASTE SEGMENT].

Write it as: a LinkedIn post (2-4 sentence paragraphs, no hashtag spam), an X thread (5-7 posts, first post standalone), an Instagram caption (front-loaded, first line must work truncated), and a newsletter section (conversational, one link).

Each must work independently. Do not just reformat the same sentences.

In practice: Reposting identical copy across platforms underperforms on all of them.

Mining the back catalogue

Here are my last [N] video titles with views and publish dates: [PASTE].

Identify which are evergreen (still relevant, not tied to a date or a version number) and rank them by recut potential — high views suggesting more demand, or low views suggesting a packaging problem rather than a content problem.

For the top 5, suggest the recut angle: new hook, new title, new thumbnail, or restructure.

In practice: The best repurposing candidate is often a good video with a bad title.