content-repurposer
Content repurposing & atomization microservice: take one long piece (blog post, article, transcript, essay) and reshape it for every channel — a numbered X/Twitter thread, a LinkedIn post, an...
content-repurposer — content atomization microservice
Takes ONE long piece (blog post, article, transcript, essay) and reshapes it for every channel while respecting each platform's length and format rules. A paid "intelligent microservice": every tool has a deterministic sentence/section-reshaping core that runs fully offline, and the generative tools optionally call an LLM (via ../_shared/llm.js) to polish, silently falling back to the heuristic. Every result is tagged mode: 'heuristic' | 'llm'.
Tools
| tool | output |
|---|---|
toThread | Numbered X/Twitter thread, each tweet ≤280 chars, hook first + CTA last. |
toLinkedIn | LinkedIn post: hook line, short paragraphs, → takeaways, CTA, hashtags (≤3000 chars). |
toInstagramCaption | IG caption: hook, short body, CTA, auto hashtag block (≤30, ≤2200 chars). |
toNewsletter | Email: subject, preview text, intro, headed sections, CTA. |
toYoutubeDescription | Hook, summary, timestamped chapter scaffold, link, keyword hashtags. |
toTikTokScript | Short-form script: 3s hook, timed beats + on-screen text, follow CTA. |
extractQuotes | The N most quotable pull-quotes (tweetable-flagged). |
keyPoints | The N distilled key points/takeaways + top keywords. |
All tools require text. Optional: title, hashtags, cta, url, handle, maxTweets, count.
Usage
import repurposer from './index.js';
await repurposer.adapters.toThread({ text: longPost, title: 'How we cut churn 40%', maxTweets: 8, cta: 'Follow for more' });
await repurposer.adapters.toInstagramCaption({ text: longPost, hashtags: ['saas', 'growth'] });
repurposer.adapters.keyPoints({ text: longPost, count: 5 });
repurposer.adapters.extractQuotes({ text: longPost, count: 3 });Each call takes ONE args object (maps 1:1 to an HTTP POST body). Missing text throws TypeError.
Hybrid intelligence
The extractive engine (sentence ranking by content-word frequency + position) always produces output. When an LLM is reachable, toLinkedIn, toInstagramCaption, and toTikTokScript polish the result and keep the heuristic as fallback. A down/absent model never throws — the pack works 100% offline.
DRY boundaries
- Reshapes one input across channels. It does not generate net-new copy from a product brief
(that's copywriter), audit HTML/meta (seo), or build keyword briefs (seo-brief).
- General NLP primitives (tokenize/summarize/sentiment) live in
nlp; this is the platform-formatting layer. - No cross-pack imports except
../_shared/llm.js. Pure ESM, Node built-ins only, zero npm deps.