/content-pillar-atomizer

name: content-pillar-atomizer
description: >
  Take 1 blog post or article and generate 15-30 platform-native micro-content pieces.
  Not reformatting — re-contextualizing for each platform's culture.
  Triggers on: "atomize this content", "repurpose my blog post", "turn this into social posts",
  "content atomizer", "pillar content", "one to many content", "repurpose content",
  "multiply my content", "content explosion", "turn article into posts",
  "break down this article", "micro content from blog", "content pillar strategy",
  "10x my content", "platform-native content", "atomize", "content multiplication".
license: MIT
version: "1.0.0"
tags: ["affiliate-marketing", "content-creation", "social-media", "copywriting", "content-strategy", "repurposing"]
compatibility: "Claude Code, ChatGPT, Gemini CLI, Cursor, Windsurf, OpenClaw, any AI agent"
metadata:
  author: affitor
  version: "1.0"
  stage: S2-Content

Content Pillar Atomizer

Take 1 blog post or article and generate 15-30 platform-native micro-content pieces. This is NOT reformatting — it's re-contextualizing each piece for the platform's culture, format, and audience expectations. A LinkedIn post reads nothing like a Reddit comment, even if they carry the same insight.

Stage

S2: Content Creation — This IS content creation, just at 10x scale. One piece of deep work becomes a month of social content.

When to Use

• User has a blog post, article, or long-form content and wants to maximize its reach
• User asks to "repurpose" or "atomize" content
• User says "turn this into social posts", "content multiplication", "pillar content"
• After `affiliate-blog-builder` (S3) produces an article — atomize it into social
• User wants to maintain consistent content output without creating from scratch daily

Input Schema

pillar_content: string        # REQUIRED — the full blog post/article text, or URL to fetch

platforms: string[]           # OPTIONAL — target platforms
                              # Options: "twitter", "linkedin", "reddit", "tiktok", "email", "threads"
                              # Default: ["twitter", "linkedin", "reddit"]

product: object               # OPTIONAL — affiliate product being promoted
  name: string
  url: string
  reward_value: string

mode: string                  # OPTIONAL — "quality" | "volume"
                              # Default: "quality"

tone: string                  # OPTIONAL — "professional" | "casual" | "edgy" | "educational"
                              # Default: inferred from pillar content

**Chaining from S3**: If `affiliate-blog-builder` was run, use its output article as `pillar_content`.

**Chaining from S1 monopoly-niche-finder**: Use `monopoly_niche` positioning to angle all micro-content.

Workflow

Step 1: Analyze Pillar Content

1. If URL provided, use `web_fetch` to retrieve content 2. Extract: key insights (5-8), data points, quotes, frameworks, stories, opinions 3. Identify the "atomic units" — self-contained ideas that work independently 4. Note the product/affiliate angle (if present)

Step 1.5: Check Platform Performance for This Topic (data-driven)

Before atomizing equally across all platforms, understand which platforms are hot for this topic:

**If `trending-content-scout` ran:**

• Use platform-level engagement data from `pattern_analysis`
• Check `engagement_benchmark.platform_averages` — which platform has highest engagement for this keyword?
• Prioritize platforms where this topic has highest engagement
• Adjust platform allocation accordingly (see below)

**Quick check (no scout data):**

• `web_search "[topic] youtube vs tiktok vs linkedin"` → which platform dominates discussion?
• Check: is this topic more visual (→ TikTok/YouTube heavy) or professional (→ LinkedIn heavy)?
• Look for: which platform shows up most in search results for this topic?

**Apply to atomization allocation:**

• Default: equal split across platforms
• Data-driven: proportional to engagement potential
• If TikTok engagement is 5x LinkedIn for this topic → generate 5 TikTok scripts, 1 LinkedIn post
• If Reddit has high engagement → don't skip Reddit (often ignored by affiliates = opportunity)
• If YouTube dominates → consider atomizing into YouTube Shorts scripts instead of just TikTok

**Platform allocation example:**

Default (no data):    Twitter: 5 | LinkedIn: 3 | Reddit: 3 | TikTok: 3 | Email: 2
Data-driven (TikTok hot): Twitter: 3 | LinkedIn: 1 | Reddit: 2 | TikTok: 6 | Email: 2
Data-driven (LinkedIn hot): Twitter: 3 | LinkedIn: 5 | Reddit: 2 | TikTok: 2 | Email: 2

Step 2: Platform Mapping

Read `shared/references/platform-rules.md` for platform-specific rules.

For each platform, map the culture:

| Platform | Format | Tone | Length | CTA Style | |---|---|---|---|---| | Twitter/X | Thread or single tweet | Punchy, opinionated | 280 chars or 5-10 tweet thread | Last tweet | | LinkedIn | Story or insight post | Professional, first-person | 1300 chars | Soft CTA in comments | | Reddit | Value-first post/comment | Helpful, honest, skeptical-aware | Variable | Disclosure + subtle | | TikTok | Script with hook | Casual, energetic | 30-60s script | Verbal + bio link | | Email | Newsletter section | Conversational | 200-400 words | Direct link | | Threads | Conversational take | Casual, authentic | 500 chars | Bio link |

Step 3: Generate Micro-Content

For each platform, generate pieces from different atomic units:

• **Twitter**: 3-5 pieces (1 thread, 2-3 standalone tweets, 1 hot take)
• **LinkedIn**: 2-3 pieces (1 story post, 1 insight post, 1 question post)
• **Reddit**: 2-3 pieces (1 detailed post, 1-2 comment-ready responses)
• **TikTok**: 2-3 scripts (1 educational, 1 hot take, 1 tutorial)
• **Email**: 1-2 pieces (newsletter section, dedicated email)
• **Threads**: 2-3 pieces (conversational takes)

Each piece must:

• Stand alone (makes sense without reading the pillar)
• Feel native to the platform (not a copy-pas