Content Pillar Atomizer

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:

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-paste resize)
• Carry one clear insight or value point
• Include appropriate FTC disclosure for affiliate content

Step 4: Tag for Tracking

Tag each piece with:

• Source pillar reference
• Platform
• Content type (thread, single, story, script)
• Affiliate product (if applicable)
• Suggested posting time/day

Step 5: Self-Validation

• Each piece feels native to its platform (not copy-pasted)
• Each piece stands alone without needing the pillar
• FTC disclosure included where affiliate links present
• No two pieces on the same platform say the same thing
• Platform rules followed (Reddit skepticism, LinkedIn professionalism, etc.)

Output Schema

output_schema_version: "1.0.0"
atomized_content:
  pillar_title: string
  total_pieces: number
  platforms_covered: string[]

  pieces:
    - platform: string
      type: string              # "thread" | "single" | "story" | "script" | "email" | "comment"
      content: string           # The actual content, ready to post
      insight_source: string    # Which atomic unit from the pillar
      has_affiliate_link: boolean
      suggested_timing: string  # e.g., "Tuesday 9am"
      variant_id: string        # For volume mode A/B tracking

  content_pillars: string[]    # Atomic units extracted (for chaining)

chain_metadata:
  skill_slug: "content-pillar-atomizer"
  stage: "content"
  timestamp: string
  suggested_next:
    - "social-media-scheduler"
    - "email-drip-sequence"
    - "ab-test-generator"

Output Format

## Content Atomizer: [Pillar Title]

### Pillar Analysis
- **Atomic units extracted:** X insights
- **Platforms:** [list]
- **Total pieces generated:** XX

---

### Twitter/X (X pieces)

**Thread: [Title]**
🧵 1/ [first tweet]
2/ [second tweet]
...
[last tweet with CTA]

**Standalone Tweet:**
[tweet text]

---

### LinkedIn (X pieces)

**Story Post:**
[full LinkedIn post]

---

### Reddit (X pieces)

**Post: r/[subreddit]**
Title: [title]
[body with disclosure]

---

[Continue for each platform]

### Posting Schedule
| Day | Platform | Piece | Time |
|---|---|---|---|
| Mon | Twitter | Thread | 9am |
| Tue | LinkedIn | Story | 8am |
| Wed | Reddit | Post | 12pm |

Error Handling

• No pillar content provided: "Paste your blog post or article, or give me the URL and I'll fetch it."
• Content too short: "This is quite short for atomization. I'll extract what I can, but consider writing a longer pillar first with affiliate-blog-builder."
• No affiliate angle: Generate content without affiliate links. Pure value content builds audience for future promotions.
• Platform not supported: "I don't have specific rules for [platform]. I'll format it generically — review before posting."