content-atomizer

Content Atomizer Skill

One piece of content should become ten. The best creators don't create more—they distribute better.

This skill transforms any source content into platform-optimized assets. Not generic repurposing. Platform-native content that performs.

The math: A single blog post can become 1 LinkedIn carousel + 2 LinkedIn text posts + 1 Twitter thread + 3 single tweets + 2 Instagram carousels + 1 Reel script + 2 TikTok scripts + 1 YouTube Short script + 1 Threads mini-thread + 1 Bluesky post + 1 Reddit value post = 16 pieces of content from one source.

Read ./brand/ per _system/brand-memory.md

Follow all output formatting rules from _system/output-format.md

---

Brand Memory Integration

This skill reads brand context to ensure every atomized piece sounds like the user's brand, adapts tone per platform, and builds on what has worked before. It also checks the learnings journal for platform performance data and the stack file for scheduling tool availability.

Reads: voice-profile.md, creative-kit.md, learnings.md, stack.md (all optional)

On invocation, check for ./brand/ and load available context:

1. Load voice-profile.md (if exists):

   • Extract tone DNA, vocabulary, sentence patterns, and formality level
   • Apply the platform adaptation table (see below) to adjust voice per platform
   • A "direct, proof-heavy" voice sounds different on LinkedIn vs TikTok vs Reddit
   • Use vocabulary lists to stay on-brand: preferred words, banned words, signature phrases
   • Show: "Your voice is [tone summary]. Adapting for each platform."

2. Load creative-kit.md (if exists):

   • Use brand colors, fonts, and visual identity for carousel slide direction
   • Reference logo placement and design system for visual content notes
   • Show: "Creative kit loaded. Visual notes will reference your brand system."

3. Load learnings.md (if exists):

   • Check for platform-specific performance data (e.g., "long-form LinkedIn posts outperform short ones")
   • Check for hook patterns that have worked or failed on specific platforms
   • Check for optimal posting times per platform
   • Check for format preferences (carousels vs text, threads vs single tweets)
   • Show: "Found [N] platform learnings. Applying: [key insight]."