version: "1.0.1" name: content-repurposing description: "Cross-format content adaptation. Turning one substantial piece into many derivative formats (blog series, email sequences, social posts, webinars, podcasts, video shorts) without losing the original's value or producing AI-slop variants. The discipline of adaptation per medium rather than mass-blast distribution. Triggers on content repurposing, content adaptation, cross-format content, content atomization, content multiplication, content distribution across formats, source-piece-to-derivative, video shorts from blog, email from whitepaper, podcast from article, blog series from research. Also triggers when a flagship piece is shipping but the team has not planned how to extend it across formats, when repurposing is happening but the derivatives feel mass-produced, or when AI-assisted repurposing is producing slop variants of strong source pieces." category: content catalog_summary: "Cross-format content adaptation: one piece becomes many (blog series, email, social, webinar, podcast, video) with per-format adaptation rather than mass-blast that ignores medium constraints" display_order: 11

Content Repurposing

A senior editorial leader's playbook for cross-format content adaptation. The discipline of turning one substantial piece into many derivative formats without losing the original's value or producing slop variants.

Most content programs underspend on repurposing. A flagship piece costs 40-80 hours to produce; the program publishes it once, shares it on three channels, and moves on. The same piece could have produced a blog series, an email sequence, a webinar, a podcast episode, a dozen social posts, video shorts, and FAQ extractions for AI search visibility. The work to extend the source piece across formats is small relative to the value extracted; programs that skip repurposing leave most of the value unrealized.

The failure mode in the other direction is mass-blast: the same content reposted across channels without adaptation. A blog post pasted into LinkedIn as a long-text post; the email newsletter is the blog's first three paragraphs with "read more" tacked on; the YouTube video is a slideshow of the article text read aloud. Mass-blast respects neither the medium nor the audience. AI-assisted repurposing has made mass-blast cheap; the result is a wave of derivative content that performs poorly across every channel because it was adapted to none of them.

This skill is the discipline of adaptation per medium. Each format has constraints, conventions, and reader expectations the source piece does not have. Repurposing that respects those constraints produces work that earns engagement on the new format; repurposing that ignores them produces filler.

When to use this skill: planning the extension of a flagship piece across formats, auditing a repurposing pipeline that is producing low-engagement derivatives, calibrating an AI-assisted repurposing workflow that is producing slop, or designing the cross-format adaptation conventions for a content program.

What this skill is for

This skill spans cross-format adaptation work after a source piece has been produced. The content suite distinction:

• content-strategy decides what to produce.
• pillar-content-architecture designs the topical hub.
• content-brief-authoring briefs each piece.
• content-and-copy writes the original piece in any single format.
• `content-repurposing` (this skill) turns one piece INTO many formats.
• content-distribution gets content TO audiences via channels.
• editorial-qa verifies pre-publish, including derivatives.
• ai-content-collaboration is the workflow layer; AI participation rules apply within repurposing.

The distinction from content-distribution is load-bearing. Distribution is channel work: getting content to audiences via the right channels. Repurposing is transformation work: turning one piece INTO many formats, each adapted for its medium. They compose: repurpose first, then distribute the right format on the right channel.

The audience: editorial leads, content directors, content ops managers, in-house teams running multi-format programs, agencies producing derivative content for clients, anyone planning to extend a flagship piece across formats.

What is not in scope: the original piece's production (covered by content-and-copy and the long-form skills), the channels themselves (covered by content-distribution), the editorial QA on derivatives (covered by editorial-qa).

One-and-done vs mass-blast vs adapt-by-format

The keystone framing.

One-and-done. Publish once on the source format; never reuse. The piece took 60 hours to produce; it generates traffic for 90 days; it gets shared three times; it goes silent. The team treats publication as the end of the piece's life. Output: most of the source piece's value goes unrealized; the team is always producing new flagship work because old flagship work is not being extended.

Mass-blast. Same content reposted across channels without adaptation. Blog post text pasted into LinkedIn as a long post. Email newsletter is the blog's first three paragraphs. The YouTube version is a slideshow of the text read by AI voice. Output: low engagement on every channel because nothing was adapted to any channel's conventions. Audiences perceive the cross-channel sameness as low-effort filler. AI-assisted repurposing has made this pattern cheap and common.

Adapt-by-format. Per-medium adaptation that respects each format's constraints and conventions. The blog series breaks the source piece into chapters with new ledes and closings per chapter. The email sequence builds on the source's framework with sender-voice adjustments. Social posts use platform-native conventions. The webinar adds Q&A and live elements. Output: each derivative earns engagement on its medium because it was made for that medium; the source piece's value compounds across formats.

The litmus test. Read each derivative as if you had not seen the source piece. Does it stand on its own? Does it use the medium's conventions? Would it earn engagement if it were the only thing the audience saw of this work? If yes to all three, the adaptation succeeded. If the derivative reads as "I should have read the original instead," the adaptation failed.

Source-piece selection

Not every piece is worth repurposing. Selection is the first discipline.

Strong source-piece characteristics.

• The source has a clear central argument or framework that travels across formats.
• The source has standalone sections, examples, or sub-arguments that work as derivative pieces.
• The source's audience overlaps with the audiences on the target formats.
• The source's voice is distinctive enough that derivative voice can stay coherent.
• The source has accumulated demand: traffic, links, mentions, social shares.

Weak source-piece characteristics.

• The source is a tightly-integrated argument where every section depends on context.
• The source is a list-style piece where the items do not have standalone weight.
• The source's value depended on being read end-to-end (a narrative arc, a layered argument with payoff).
• The source's audience does not exist on most target formats.
• The source's voice is generic; derivative voice has nothing to anchor.

The selection audit. Run candidate pieces through these questions before committing to repurposing. Pieces that fail the audit may still be valuable but as one-and-done; programs that try to repurpose unsuitable pieces produce derivatives that feel forced.

Detail in `references/source-piece-selection-criteria.md`.

Format adaptation patterns

Eight common source-to-derivative adaptations with worked examples.

Long-form to blog series. A 6,000-word whitepaper becomes 4-6 standalone blog posts, each developing one of the whitepaper's sub-arguments with new ledes and closings.

Blog post to email sequence. A 1,500-word blog post on a multi-step framework becomes a 5-email sequence, one email per step, with sender-voice adaptation and per-email engagement hooks.

Whitepaper to webinar. Substantive whitepaper becomes a 30-45 minute webinar with the whitepaper's framework as the spine, plus live Q&A, plus interactive elements that print do not support.

Long-form to social posts. Pull-quote-style posts, framework summaries, key-question posts, contrarian-claim posts. Each social post is one moment from the source, framed for the platform's conventions.

Article to podcast episode. Article becomes the episode's spine; the host adds context, examples, and conversational elaboration; sometimes a guest interview drives the episode while the article is the show notes.

Long-form to video shorts. 60-90 second video clips on individual claims, examples, or framework elements from the source. Each short is a standalone unit; series-of-shorts can extend the source over weeks of social posting.

Research report to FAQ extractions. Specific Q&A extractions from the source piece, formatted for AI search citation and snippet capture.

Multi-piece source to ebook. Several related pieces (blog posts, articles, even social threads) consolidated into an ebook, with new connective tissue, an introduction that frames the body, and a conclusion that synthesizes.

Detail in `references/format-adaptation-patterns.md`.

Per-format constraints

Each medium demands and forbids specific things. Repurposing that ignores the constraints produces derivatives that fail the medium.

Email.

• Subject line and preheader bear most of the engagement load.
• Plain prose paragraphs work; complex formatting often breaks across email clients.
• Length tolerance varies by audience; 200-800 words common.
• Single clear CTA per email; multiple competing CTAs underperform.

Social posts (text-driven platforms).

• Hook in the first 1-2 lines; readers decide to expand or scroll.
• Platform-specific conventions (LinkedIn long-post structure differs from X/Twitter thread structure).
• Visual or quote-graphic options to break up text.
• One specific point per post; multi-point posts dilute.

Video (long-form).

• Pacing differs from print; spoken delivery requires different sentence structure.
• Visuals carry information print does not need; talking-head video alone for 30+ minutes underperforms.
• Audio quality matters more than video quality up to a point; bad audio kills retention.

Video (short-form).

• 60-90 second window for most platforms; 15-30 seconds for some.
• Captions essential; many viewers watch without sound.
• Hook in the first 1-2 seconds; algorithmic recommendation rewards retention from second 1.
• One specific point or moment per short.

Podcast.

• Spoken-language sentence structure; print-style sentences read aloud sound stilted.
• Conversational pacing rewards examples and asides; tightly-argued print sections feel stiff.
• Show notes do the print work the audio cannot; treat show notes as a derivative format too.

Webinar.

• Live element: Q&A, polls, interactive moments. Static webinar is a video; interactive webinar earns the format.
• Slide design matters; text-heavy slides fail.
• Length: 30-60 minutes typical; longer requires high engagement design.

Detail in `references/per-format-constraints.md`.

Voice consistency across formats

The source piece's voice anchors the derivatives. The discipline is staying recognizable through the format shifts.

What stays constant.

• The brand's POV on the topic.
• Distinctive vocabulary, specific framings, recognizable phrasings.
• The level of conviction or hedging the brand uses.
• The audience-respect register (talking up to, down to, alongside the audience).

What adapts per format.

• Sentence length and structure (spoken voice differs from written).
• Density (visual formats can carry less per minute than text).
• Conversational vs declarative register (podcasts are conversational; whitepapers declarative).
• Direct-address frequency ("you" usage varies by format).

The voice audit per derivative. Read or watch the derivative. Does it sound like the brand? Could a reader who knows the source piece tell the derivative is from the same source? If voice has drifted to AI-default or to the platform's default register, the adaptation lost the voice.

The AI-repurposing voice problem. AI-assisted repurposing is particularly prone to voice drift. The source piece may have specific voice characteristics; AI generation of derivatives without strong voice prompts produces derivatives that sound more generic than the source. See ai-content-collaboration for the voice-preservation discipline that applies to repurposing workflows.

Detail in `references/voice-consistency-across-formats.md`.

Sequencing and cadence

When to release derivatives relative to the source, and at what pace.

Sequencing patterns.

• Source-first, derivatives following. Source publishes; derivatives roll out over the following 2-12 weeks. The source carries authority; derivatives extend reach.
• Derivatives-first, source as anchor. Social posts and short-form video tease ideas; the source piece publishes as the synthesizing flagship. Less common; works for built-up audiences who follow the program.
• Simultaneous launch. Source and a wave of derivatives publish on the same day. Maximum splash; risks audience fatigue if every channel is firing the same day.

Cadence within the rollout.

• Concentrated. Most derivatives ship within 4-6 weeks of source. Maximum momentum from the source's authority; minimum time for individual derivatives to land.
• Distributed. Derivatives ship over 3-6 months. Each derivative has space to land; the program is extracting value over a longer window; risks losing connection to source if the gap grows too long.
• Indefinite. Derivatives keep being created from the source as long as the source remains relevant. Common for evergreen flagship pieces; rare otherwise.

The pacing audit. Match cadence to the source's traffic curve. Pieces with sharp early traffic (trending topics) benefit from concentrated rollout; pieces with sustained evergreen traffic can support distributed rollout over many months.

Detail in `references/sequencing-and-cadence-patterns.md`.

Cross-promotion across derivatives

Derivatives that link to each other and to the source compound. Derivatives that ship in isolation underperform.

Linking patterns.

• Derivative back to source. Each derivative includes a link or reference to the source piece for readers who want depth. The source's traffic compounds.
• Source forward to derivatives. The source piece is updated to include links to derivatives as they ship. The source becomes the hub for the cross-format extension.
• Derivatives across. Where appropriate, derivatives link to other derivatives in the cross-format set. The series feeling helps audiences engage with multiple pieces.

Attribution within derivatives. When a derivative is clearly drawn from a source piece, acknowledge: "This is adapted from our recent piece on X." Attribution earns reader trust; uncredited derivatives can feel like the same work being recycled without acknowledgment.

Co-promotion across channels. A blog post derivative can be re-shared on social where the original blog post was; a social post derivative can be promoted in the email newsletter. Cross-channel flow extends each derivative's reach.

Detail in `references/cross-promotion-patterns.md`.

Repurposing for AI search visibility

A specific repurposing pattern worth its own treatment.

FAQ extraction. Pull specific question-answer pairs from the source piece. Each Q&A is 40-80 words. Format as standalone FAQ entries that can be cited by AI search engines.

Snippet design. Identify standalone paragraphs in the source that answer specific queries cleanly. These paragraphs can be quoted, schema-marked, and presented in derivative pieces or in dedicated FAQ pages.

Statistic extractions. AI engines weight statistics with named sources. Extract specific stats from the source (with citations to the underlying primary research) into format that AI can cite cleanly.

Definition and entity extractions. Source pieces often define specific terms or describe specific entities authoritatively. Extract these as standalone definitions in glossaries, FAQ pages, or knowledge-base entries.

The AI-search-derivative discipline. Treat AI-search optimization as a derivative format with its own conventions: specific question framings, named-source citations, standalone-paragraph design. Pieces that do this well earn AI citations; pieces that do not still get cited but at lower rates and with less control over framing.

Detail in `references/aeo-extraction-patterns.md`.

Common failure modes

Rapid-fire. Diagnoses in `references/common-repurposing-failures.md`.

• "Our derivatives feel like AI slop." Mass-blast pattern; AI-assisted repurposing without per-format adaptation. Cure: add adaptation discipline, voice prompts, per-format review.
• "We repurposed but engagement is low." Format constraints ignored. Cure: audit each derivative against per-format conventions.
• "The blog post derivative outperforms the source." Either the source was the wrong format for the topic (the derivative format fits better), or the derivative cannibalizes the source. Investigate.
• "We never reuse pieces." One-and-done pattern; the program is leaving 70-90% of source-piece value unrealized.
• "Our LinkedIn posts are blog-post text dumps." Mass-blast on a platform with specific conventions. Cure: rewrite for platform-native structure.
• "The email newsletter is the blog's first paragraphs with 'read more' tacked on." Mass-blast email pattern. Cure: rewrite as a standalone email with engagement hooks.
• "Our podcast episodes are articles read aloud." Print-to-audio without spoken-language adaptation. Cure: rewrite for spoken delivery; add conversational pacing.
• "We extracted FAQ pages but they are not getting cited by AI search." Format may be wrong (snippets not standalone) or content may not be authoritative enough; audit against AI-search norms.
• "Our voice drifts across derivatives." AI-repurposing voice failure. Cure: stronger voice prompts; per-derivative voice audit.
• "We launched 12 derivatives in a week and audiences were exhausted." Concentrated cadence too aggressive. Cure: distributed cadence with engagement-driven sequencing.
• "Our derivatives compete with the source for the same query." Cannibalization. Cure: clear primary-keyword assignment per piece; consolidate or restructure.

The framework: 12 considerations for content repurposing

When designing or auditing a repurposing program, walk these 12 considerations.

1. Adapt-by-format, not mass-blast or one-and-done. Each derivative respects its medium.
2. Source-piece selection. Strong sources have travelable arguments and standalone sections.
3. Format adaptation patterns chosen. Match source-piece characteristics to derivative formats.
4. Per-format constraints respected. Each medium's conventions, length norms, and reader expectations.
5. Voice consistency across formats. Brand POV stays; sentence structure adapts.
6. Sequencing and cadence planned. Source-first vs simultaneous; concentrated vs distributed.
7. Cross-promotion across derivatives. Linking, attribution, co-promotion.
8. AI-search extraction. FAQ, snippet, statistic, entity extractions designed deliberately.
9. Cannibalization avoided. Derivatives complement the source; do not compete for the same query.
10. Engagement-per-format measured. Derivatives evaluated on each medium's metrics.
11. AI-assisted repurposing with voice discipline. Voice prompts, per-derivative review, slop prevention.
12. Capacity allocation explicit. Repurposing is real work; budgeted, not done in the cracks.

The output of the framework is a repurposing program that turns each strong source piece into a coherent multi-format extension, with each derivative earning engagement on its medium.

If required data is unavailable

This skill's output depends on data, measurements, or tool results it cannot generate on its own. When a required input, tool, or data source is unavailable or unverifiable, the sanctioned output is the deliverable with the gap stated: what was needed, what was actually obtained or verified, and which parts of the output are affected. Fabricating, estimating, or interpolating a required number to complete the deliverable is never sanctioned. A stated gap is a complete answer.

Reference files

• `references/source-piece-selection-criteria.md` - Strong vs weak source-piece characteristics. The selection audit. Pieces that should stay one-and-done.
• `references/format-adaptation-patterns.md` - Eight source-to-derivative adaptations with worked examples. Long-form to blog series, blog to email, whitepaper to webinar, etc.
• `references/per-format-constraints.md` - What each medium demands and forbids. Email, social, video, podcast, webinar, AI search. Length norms, conventions, hooks.
• `references/voice-consistency-across-formats.md` - What stays constant and what adapts. The voice audit per derivative. The AI-repurposing voice problem.
• `references/sequencing-and-cadence-patterns.md` - Source-first vs simultaneous vs derivative-first. Concentrated vs distributed cadence. Pacing audits.
• `references/cross-promotion-patterns.md` - Linking patterns, attribution, co-promotion across channels.
• `references/aeo-extraction-patterns.md` - FAQ extraction, snippet design, statistic extractions, entity extractions for AI search citation.
• `references/repurposing-pipeline-templates.md` - Workflow templates by source-piece type. Whitepaper pipeline, blog post pipeline, research report pipeline, multi-piece consolidation pipeline.
• `references/common-repurposing-failures.md` - 11+ failure patterns with diagnoses and fixes.

Closing: adaptation is craft, not duplication

Repurposing is widely treated as a content multiplication problem to be solved with AI: feed the source into a tool, get derivatives out. The output of that approach is mass-blast, slop, and audiences that learn to ignore the program. The teams producing repurposing that earns engagement are the ones treating each derivative as a piece in its own right, adapted for its medium, written with the source's voice but the format's craft.

Adaptation is craft, not duplication. The source piece is the starting material; the derivative is its own work. Programs that hold this discipline get value compounding across formats; programs that skip it produce a wall of derivative content that performs worse, not better, than publishing only the source.

When in doubt about whether a repurposing program is ready, ask: does each derivative respect its medium's conventions, does voice stay consistent across the set, are derivatives cross-promoting and linking back to the source, is AI-search extraction part of the plan, is cannibalization being managed, is the cadence pacing earning engagement? If yes to all of those, the program is real. If no to any, the gap is where the derivatives will read as filler and audiences will tune out.