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content-repurpose

Turn one piece of content into a multi-channel repurposing plan — a derivative matrix targeting 10+ formats, full platform-adapted drafts, a publishing calendar, UTM-tagged links, and per-piece brand-voice scores; every derivative must pass the standalone test (own hook, own payoff) and the cut list…

Install / Use

npx skills add indranilbanerjee/digital-marketing-pro --skill content-repurpose

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

82/100

Supported Platforms

Universal

Our assessment of content-repurpose

content-repurpose scores 82/100 on our quality scale, 990th of 1,213 Content & Media skills we index.

Its SKILL.md is 7.4 KB long, split into 6 sections and no code examples: a thorough specification that gives an agent plenty to work with.

It has 832 GitHub stars, a meaningful sign that others use it.

Substance
29/30
Structure
11/20
Description
15/15
Adoption
12/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 26 days ago, so content-repurpose is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

content-repurpose compared with similar skills

All 4 of these similar skills score higher than content-repurpose; compare them before choosing.

SkillScoreStarsUpdatedFormat
content-repurpose (this skill)by indranilbanerjee8283226d agoSKILL.md
Agent-Reachby Panniantong10090.1k18d agoCLAUDE.md
LocalAIby mudler10049.4ktodayMCP Server
siyuanby siyuan-note10046.6ktodayMCP Server
algorithmic-artby anthropics100177.9k11d agoSKILL.md

Frequently asked questions

How do I install content-repurpose?
Run npx skills add indranilbanerjee/digital-marketing-pro --skill content-repurpose. The install tabs above show the steps for each supported agent.
Which AI agents does content-repurpose work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is content-repurpose safe to use?
It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is content-repurpose still maintained?
The repository was last updated 26 days ago, so content-repurpose is actively maintained.

name: content-repurpose description: "Turn one piece of content into a multi-channel repurposing plan — a derivative matrix targeting 10+ formats, full platform-adapted drafts, a publishing calendar, UTM-tagged links, and per-piece brand-voice scores; every derivative must pass the standalone test (own hook, own payoff) and the cut list records what failed it. Triggers on "/digital-marketing-pro:content-repurpose", "repurpose this blog post", "turn this webinar into social posts", "get more mileage out of this article", "atomize this whitepaper". Produces drafts and a schedule, not published posts. Reads the brand profile, channel style overrides, and platform specs."

/digital-marketing-pro:content-repurpose

Purpose

Take one piece of existing content and generate a comprehensive repurposing plan across multiple channels and formats. Produces derivative content pieces, a posting schedule, and platform-specific adaptations to maximize the ROI of every content investment.

Input Required

The user must provide (or will be prompted for):

  • Original content: The source material -- a URL, pasted text, uploaded document, or description of the content (blog post, webinar recording, podcast episode, whitepaper, case study, presentation, video, etc.)
  • Target channels: Which platforms and formats to repurpose into (LinkedIn, Twitter/X, Instagram, email newsletter, blog, YouTube, TikTok, podcast, infographic, etc.) or ask for recommendations
  • Brand voice context: Tone and style preferences (auto-loaded from brand profile if available)
  • Priority goals: What the repurposed content should achieve (traffic, engagement, lead gen, thought leadership, SEO backlinks)
  • Timeline: How quickly the repurposed content needs to go live (same day, one week, two weeks, ongoing drip)
  • Constraints: Any platforms to exclude, content restrictions, compliance requirements, or approval workflows
  • Content performance data: Optional -- engagement metrics from the original piece to identify strongest elements

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Apply voice, compliance, industry context. Check guidelines/_manifest.json for restrictions, messaging, channel styles, voice-and-tone rules, and templates. If a template matching this command exists in ~/.claude-marketing/brands/{slug}/templates/, apply its format. If no brand exists, prompt for /digital-marketing-pro:brand-setup or proceed with defaults.
  2. Check campaign history: Run python "${CLAUDE_PLUGIN_ROOT}/scripts/campaign-tracker.py" --brand {slug} --action list-campaigns to identify related campaigns and previously published content that derivative pieces can reference or link to.
  3. Analyze original content: Extract the core elements -- key messages, data points, compelling quotes, statistics, step-by-step processes, visual concepts, storytelling hooks, counterintuitive insights, and main takeaways. Identify which elements are strongest for each target format.
  4. Map to channel-specific formats: Build a repurposing matrix mapping the original content to derivative formats: blog to social threads, webinar to blog series, podcast to audiograms, whitepaper to infographic, case study to testimonial posts, presentation to carousel posts, long-form to short-form snippets, and vice versa. Target 10+ derivative pieces per source — but the target never overrides the standalone test below; eight strong pieces beat twelve where four are filler. 4.5. Apply the standalone test — and cut what fails it: Every derivative piece must work for someone who will never see the source: its own hook, its own payoff, no context debt ("as we discussed in the full article" is a failure). Not every section of a source is repurposable — a passage that only works inside the original's argument is not a weak derivative waiting for better editing, it is not a derivative at all. List what was cut and why alongside the matrix; the cut list is evidence the filter ran. Rank survivors by how well they stand alone, and lead the calendar with the strongest.
  5. Apply platform specifications: Reference skills/context-engine/platform-specs.md for character limits, image dimensions, video lengths, hashtag best practices, and format requirements per platform. Adapt each piece to fit native platform conventions.
  6. Adapt messaging for each format: Rewrite and restructure content for each derivative piece -- not simple truncation but genuine adaptation. A LinkedIn post needs a different hook and structure than a Twitter/X thread, which differs from an email newsletter excerpt or an Instagram carousel. Match the native content style of each platform.
  7. Apply channel-specific voice overrides: If brand guidelines include channel-styles.md, apply platform-specific tone adjustments (e.g., more casual on social, more authoritative in email, more concise on Twitter/X).
  8. Generate content calendar for repurposed pieces: Sequence the derivative content across a publishing timeline. Space out related pieces to avoid audience fatigue. Front-load high-impact formats and follow with supporting pieces. Align with optimal posting times per platform.
  9. Score each variant for brand voice alignment: Check every derivative piece against brand voice settings (formality, energy, humor, authority) and channel-specific style overrides from guidelines. Flag any pieces that drift from established voice.
  10. Add tracking and attribution: Attach UTM parameters to all links in derivative content so traffic driven back to the original or landing pages can be attributed to the specific repurposed piece and platform.
  11. Define performance metrics per format: Set engagement benchmarks for each derivative piece (impressions, clicks, shares, saves, comments) based on platform averages and brand historical performance.

Output

A structured content repurposing plan containing:

  • Original content summary with extracted key elements (messages, data, quotes, hooks, takeaways)
  • Repurposing matrix mapping original content to 10+ derivative formats across channels
  • Full draft content for each derivative piece, adapted to platform conventions and native style
  • Platform-specific formatting notes (character counts, image specs, hashtag sets, posting format)
  • Publishing calendar with recommended dates, times, and sequencing logic
  • Brand voice alignment score for each piece with adjustment notes where needed
  • Cross-linking strategy connecting derivative pieces back to the original and to each other
  • Estimated reach and engagement projections per format based on channel benchmarks
  • UTM-tagged links for each derivative piece enabling attribution tracking
  • Performance benchmarks per format with success criteria for each piece
  • Visual asset requirements per derivative piece (image dimensions, video specs, design notes)
  • Hashtag and keyword recommendations per platform for discoverability
  • Suggested engagement hooks and CTAs tailored to each platform's audience behavior

Agents Used

  • content-creator -- Content analysis, derivative content writing, format adaptation, voice alignment, editorial calendar planning, and cross-linking strategy
  • social-media-manager -- Platform-specific formatting, social post drafting, hashtag strategy, posting schedule optimization, engagement hook design, and cross-platform coordination

Related Skills

View on GitHub
GitHub Stars832
CategoryContent
Updated26d ago
Forks136

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions