What this prompt does
This prompt repurposes one piece of technical content into [format_count] formats so a single deep tutorial reaches many audiences. You provide the [content_type], [topic], and [source_content], and the AI generates a Twitter/X thread of [tweet_count] tweets, a LinkedIn post, a Reddit post for [subreddit], a Dev.to article, a [video_length]-minute YouTube script with visual cues, a newsletter section, a repo README, a [slide_count]-slide deck outline, an infographic brief, and a 60-second short-form script for [short_platform] — each adapted in tone while preserving technical accuracy.
The structure works because the economics of deep technical content are brutal: one good tutorial costs a day to write, then usually lives on a single page. By forcing platform-specific adaptation — value-first for Reddit, lessons-learned for LinkedIn, hooks and code for the thread — the prompt respects that each audience expects a different shape. The explicit "maintain technical accuracy across all formats" instruction guards against the usual drift where repurposed content gets vaguer with each remix.
When to use it
- You wrote a substantial tutorial and want maximum reach from a single research investment.
- You need a Twitter/X thread of
[tweet_count]tweets with hooks and code snippets. - You want a Reddit post for
[subreddit]that reads value-first, not promotional. - You need a
[video_length]-minute video script with visual cue notes. - You want a meetup deck outline of
[slide_count]slides from existing material. - You need a 60-second short for
[short_platform]without losing accuracy.
Example output
Expect [format_count] distinct deliverables, each formatted for its platform: a numbered thread with hooks, a LinkedIn post in a lessons-learned frame, a Reddit post that leads with value, a Dev.to-styled article, a video script with [video_length]-minute pacing and visual cues, a newsletter blurb, a README, a [slide_count]-slide outline, an infographic brief, and a short-form script.
Pro tips
- Paste real, complete
[source_content]rather than a summary — the richer the source, the more accurate and specific every derived format will be. - Tune
[tweet_count]to the depth of your material; padding a thread to hit a number dilutes the hooks that make people read on. - For the
[subreddit]post, lean into the value-first framing the prompt enforces — overt self-promotion gets removed by moderators and readers alike. - Keep the technical claims consistent across formats; if you spot drift in one output, ask the model to re-derive it from the source rather than the thread.
- Match
[short_platform]to where your audience watches; a 60-second script for the wrong platform wastes the format. - After generation, ask the model to flag which two formats best fit your specific
[topic]so you can prioritize the highest-leverage channels rather than publishing all ten at once.
